Journal articles on the topic 'Heart Failure, Machine Learning, Decision Support, Telemonitoring'
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Nagavelli, Umarani, Debabrata Samanta, and Partha Chakraborty. "Machine Learning Technology-Based Heart Disease Detection Models." Journal of Healthcare Engineering 2022 (February 27, 2022): 1–9. http://dx.doi.org/10.1155/2022/7351061.
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At present, a multifaceted clinical disease known as heart failure disease can affect a greater number of people in the world. In the early stages, to evaluate and diagnose the disease of heart failure, cardiac centers and hospitals are heavily based on ECG. The ECG can be considered as a regular tool. Heart disease early detection is a critical concern in healthcare services (HCS). This paper presents the different machine learning technologies based on heart disease detection brief analysis. Firstly, Naïve Bayes with a weighted approach is used for predicting heart disease. The second one, according to the features of frequency domain, time domain, and information theory, is automatic and analyze ischemic heart disease localization/detection. Two classifiers such as support vector machine (SVM) with XGBoost with the best performance are selected for the classification in this method. The third one is the heart failure automatic identification method by using an improved SVM based on the duality optimization scheme also analyzed. Finally, for a clinical decision support system (CDSS), an effective heart disease prediction model (HDPM) is used, which includes density-based spatial clustering of applications with noise (DBSCAN) for outlier detection and elimination, a hybrid synthetic minority over-sampling technique-edited nearest neighbor (SMOTE-ENN) for balancing the training data distribution, and XGBoost for heart disease prediction. Machine learning can be applied in the medical industry for disease diagnosis, detection, and prediction. The major purpose of this paper is to give clinicians a tool to help them diagnose heart problems early on. As a result, it will be easier to treat patients effectively and avoid serious repercussions. This study uses XGBoost to test alternative decision tree classification algorithms in the hopes of improving the accuracy of heart disease diagnosis. In terms of precision, accuracy, f1-measure, and recall as performance parameters above mentioned, four types of machine learning (ML) models are compared.
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Rahman, Mohammed Ashikur, and Afidalina Tumian. "Variables Influencing Machine Learning-Based Cardiac Decision Support System: A Systematic Literature Review." Applied Mechanics and Materials 892 (June 2019): 274–83. http://dx.doi.org/10.4028/www.scientific.net/amm.892.274.
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Now a day, clinical decision support systems (CDSS) are widely used in the cardiac care due to the complexity of the cardiac disease. The objective of this systematic literature review (SLR) is to identify the most common variables and machine learning techniques used to build machine learning-based clinical decision support system for cardiac care. This SLR adopts the Preferred Reporting Item for Systematic Review and Meta-Analysis (PRISMA) format. Out of 530 papers, only 21 papers met the inclusion criteria. Amongst the 22 most common variables are age, gender, heart rate, respiration rate, systolic blood pressure and medical information variables. In addition, our results have shown that Simplified Acute Physiology Score (SAPS), Sequential Organ Failure Assessment (SOFA) and Acute Physiology and Chronic Health Evaluation (APACHE) are some of the most common assessment scales used in CDSS for cardiac care. Logistic regression and support vector machine are the most common machine learning techniques applied in CDSS to predict mortality and other cardiac diseases like sepsis, cardiac arrest, heart failure and septic shock. These variables and assessment tools can be used to build a machine learning-based CDSS.
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Tuan Le, Minh, Minh Thanh Vo, Nhat Tan Pham, and Son V.T Dao. "Predicting heart failure using a wrapper-based feature selection." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 3 (March 10, 2021): 1530. http://dx.doi.org/10.11591/ijeecs.v21.i3.pp1530-1539.
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In the current health system, it is very difficult for medical practitioners/physicians to diagnose the effectiveness of heart contraction. In this research, we proposed a machine learning model to predict heart contraction using an artificial neural network (ANN). We also proposed a novel wrapper-based feature selection utilizing a grey wolf optimization (GWO) to reduce the number of required input attributes. In this work, we compared the results achieved using our method and several conventional machine learning algorithms approaches such as support vector machine, decision tree, K-nearest neighbor, naïve bayes, random forest, and logistic regression. Computational results show not only that much fewer features are needed, but also higher prediction accuracy can be achieved around 87%. This work has the potential to be applicable to clinical practice and become a supporting tool for doctors/physicians.
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B, Kavyashree, and Rakesh M D. "Prediction of Cardiac Arrhythmia using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 9 (September 30, 2022): 1698–706. http://dx.doi.org/10.22214/ijraset.2022.46900.
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Abstract: The Heart is one of the most important organ responsible for sustaining Human life. The Normal functioning of it is very important but the irregular functioning of it will causes few problems which may be classified as different heart disease. Arrhythmia an Irregular Heart Beat, which is considered as one of the Cardio Vascular Disease. Electrocardiogram (ECG) is the most preferred tool used to capture Heart Beat. Without taking proper pre-cautionary measures this may lead to sudden death, blood clots, heart failure, stroke, etc.. Machine learning is the study of computer algorithms. In this work by adopting Machine learning algorithms such as Logistic Regression, Decision Tree, SVM[Support Vector Machine]are done to foresee the Cardiac Arrhythmia. The data-sets are collected from UCI Repository & processed using python programming .From all the three applied algorithms the SVM model showed the better results of 91.41\% in terms of accuracy for 80/20 combinations of Train and Test data sets. Therefore from this work SVM model is considered as best algorithm for the prediction of Cardiac Arrhythmia.
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Sax, Dana R., Lillian R. Sturmer, Dustin G. Mark, Jamal S. Rana, and Mary E. Reed. "Barriers and Opportunities Regarding Implementation of a Machine Learning-Based Acute Heart Failure Risk Stratification Tool in the Emergency Department." Diagnostics 12, no. 10 (October 11, 2022): 2463. http://dx.doi.org/10.3390/diagnostics12102463.
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Hospital admissions for patients with acute heart failure (AHF) remain high. There is an opportunity to improve alignment between patient risk and admission decision. We recently developed a machine learning (ML)-based model that stratifies emergency department (ED) patients with AHF based on predicted risk of a 30-day severe adverse event. Prior to deploying the algorithm and paired clinical decision support, we sought to understand barriers and opportunities regarding successful implementation. We conducted semi-structured interviews with eight front-line ED providers and surveyed 67 ED providers. Audio-recorded interviews were transcribed and analyzed using thematic analysis, and we had a 65% response rate to the survey. Providers wanted decision support to be streamlined into workflows with minimal disruptions. Most providers wanted assistance primarily with ED disposition decisions, and secondarily with medical management and post-discharge follow-up care. Receiving feedback on patient outcomes after risk tool use was seen as an opportunity to increase acceptance, and few providers (<10%) had significant hesitations with using an ML-based tool after education on its use. Engagement with key front-line users on optimal design of the algorithm and decision support may contribute to broader uptake, acceptance, and adoption of recommendations for clinical decisions.
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Muntasir Nishat, Mirza, Fahim Faisal, Ishrak Jahan Ratul, Abdullah Al-Monsur, Abrar Mohammad Ar-Rafi, Sarker Mohammad Nasrullah, Md Taslim Reza, and Md Rezaul Hoque Khan. "A Comprehensive Investigation of the Performances of Different Machine Learning Classifiers with SMOTE-ENN Oversampling Technique and Hyperparameter Optimization for Imbalanced Heart Failure Dataset." Scientific Programming 2022 (March 9, 2022): 1–17. http://dx.doi.org/10.1155/2022/3649406.
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Heart failure is a chronic cardiac condition characterized by reduced supply of blood to the body due to impaired contractile properties of the muscles of the heart. Like any other cardiac disorder, heart failure is a serious ailment limiting the activities and curtailing the lifespan of the patient, most often resulting in death sooner or later. Detection of survival of patients with heart failure is the path to effective intervention and good prognosis in terms of both treatment and quality of life of the patient. Machine learning techniques can be critical in this regard since they can be used to predict the survival of patients with heart failure in advance, allowing patients to receive appropriate treatment. Hence, six supervised machine learning algorithms have been studied and applied to analyze a dataset of 299 individuals from the UCI Machine Learning Repository and predict their survivability from heart failure. Three distinct approaches have been followed using Decision Tree Classifier, Logistic Regression, Gaussian Naïve Bayes, Random Forest Classifier, K-Nearest Neighbors, and Support Vector Machine algorithms. Data scaling has been performed as a preprocessing step utilizing the standard and min–max scaling method. However, grid search cross-validation and random search cross-validation techniques have been employed to optimize the hyperparameters. Additionally, the synthetic minority oversampling technique and edited nearest neighbor (SMOTE-ENN) data resampling technique are utilized, and the performances of all the approaches have been compared extensively. The experimental results clearly indicate that Random Forest Classifier (RFC) surpasses all other approaches with a test accuracy of 90% when used in combination with SMOTE-ENN and standard scaling technique. Therefore, this comprehensive investigation portrays a vivid visualization of the applicability and compatibility of different machine learning algorithms in such an imbalanced dataset and presents the role of the SMOTE-ENN algorithm and hyperparameter optimization for enhancing the performances of the machine learning algorithms.
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Hussain, Lal, Imtiaz Ahmed Awan, Wajid Aziz, Sharjil Saeed, Amjad Ali, Farukh Zeeshan, and Kyung Sup Kwak. "Detecting Congestive Heart Failure by Extracting Multimodal Features and Employing Machine Learning Techniques." BioMed Research International 2020 (February 18, 2020): 1–19. http://dx.doi.org/10.1155/2020/4281243.
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The adaptability of heart to external and internal stimuli is reflected by the heart rate variability (HRV). Reduced HRV can be a predictor of negative cardiovascular outcomes. Based on the nonlinear, nonstationary, and highly complex dynamics of the controlling mechanism of the cardiovascular system, linear HRV measures have limited capability to accurately analyze the underlying dynamics. In this study, we propose an automated system to analyze HRV signals by extracting multimodal features to capture temporal, spectral, and complex dynamics. Robust machine learning techniques, such as support vector machine (SVM) with its kernel (linear, Gaussian, radial base function, and polynomial), decision tree (DT), k-nearest neighbor (KNN), and ensemble classifiers, were employed to evaluate the detection performance. Performance was evaluated in terms of specificity, sensitivity, positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC). The highest performance was obtained using SVM linear kernel (TA = 93.1%, AUC = 0.97, 95% CI [lower bound = 0.04, upper bound = 0.89]), followed by ensemble subspace discriminant (TA = 91.4%, AUC = 0.96, 95% CI [lower bound 0.07, upper bound = 0.81]) and SVM medium Gaussian kernel (TA = 90.5%, AUC = 0.95, 95% CI [lower bound = 0.07, upper bound = 0.86]). The results reveal that the proposed approach can provide an effective and computationally efficient tool for automatic detection of congestive heart failure patients.
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Al Mudawi, Naif, and Abdulwahab Alazeb. "A Model for Predicting Cervical Cancer Using Machine Learning Algorithms." Sensors 22, no. 11 (May 29, 2022): 4132. http://dx.doi.org/10.3390/s22114132.
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A growing number of individuals and organizations are turning to machine learning (ML) and deep learning (DL) to analyze massive amounts of data and produce actionable insights. Predicting the early stages of serious illnesses using ML-based schemes, including cancer, kidney failure, and heart attacks, is becoming increasingly common in medical practice. Cervical cancer is one of the most frequent diseases among women, and early diagnosis could be a possible solution for preventing this cancer. Thus, this study presents an astute way to predict cervical cancer with ML algorithms. Research dataset, data pre-processing, predictive model selection (PMS), and pseudo-code are the four phases of the proposed research technique. The PMS section reports experiments with a range of classic machine learning methods, including decision tree (DT), logistic regression (LR), support vector machine (SVM), K-nearest neighbors algorithm (KNN), adaptive boosting, gradient boosting, random forest, and XGBoost. In terms of cervical cancer prediction, the highest classification score of 100% is achieved with random forest (RF), decision tree (DT), adaptive boosting, and gradient boosting algorithms. In contrast, 99% accuracy has been found with SVM. The computational complexity of classic machine learning techniques is computed to assess the efficacy of the models. In addition, 132 Saudi Arabian volunteers were polled as part of this study to learn their thoughts about computer-assisted cervical cancer prediction, to focus attention on the human papillomavirus (HPV).
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Haq, Amin Ul, Jian Ping Li, Muhammad Hammad Memon, Shah Nazir, and Ruinan Sun. "A Hybrid Intelligent System Framework for the Prediction of Heart Disease Using Machine Learning Algorithms." Mobile Information Systems 2018 (December 2, 2018): 1–21. http://dx.doi.org/10.1155/2018/3860146.
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Heart disease is one of the most critical human diseases in the world and affects human life very badly. In heart disease, the heart is unable to push the required amount of blood to other parts of the body. Accurate and on time diagnosis of heart disease is important for heart failure prevention and treatment. The diagnosis of heart disease through traditional medical history has been considered as not reliable in many aspects. To classify the healthy people and people with heart disease, noninvasive-based methods such as machine learning are reliable and efficient. In the proposed study, we developed a machine-learning-based diagnosis system for heart disease prediction by using heart disease dataset. We used seven popular machine learning algorithms, three feature selection algorithms, the cross-validation method, and seven classifiers performance evaluation metrics such as classification accuracy, specificity, sensitivity, Matthews’ correlation coefficient, and execution time. The proposed system can easily identify and classify people with heart disease from healthy people. Additionally, receiver optimistic curves and area under the curves for each classifier was computed. We have discussed all of the classifiers, feature selection algorithms, preprocessing methods, validation method, and classifiers performance evaluation metrics used in this paper. The performance of the proposed system has been validated on full features and on a reduced set of features. The features reduction has an impact on classifiers performance in terms of accuracy and execution time of classifiers. The proposed machine-learning-based decision support system will assist the doctors to diagnosis heart patients efficiently.
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Jasinska-Piadlo, A., R. Bond, P. Biglarbeigi, R. Brisk, P. Campbell, and D. McEneaneny. "What can machines learn about heart failure? A systematic literature review." International Journal of Data Science and Analytics 13, no. 3 (December 30, 2021): 163–83. http://dx.doi.org/10.1007/s41060-021-00300-1.
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AbstractThis paper presents a systematic literature review with respect to application of data science and machine learning (ML) to heart failure (HF) datasets with the intention of generating both a synthesis of relevant findings and a critical evaluation of approaches, applicability and accuracy in order to inform future work within this field. This paper has a particular intention to consider ways in which the low uptake of ML techniques within clinical practice could be resolved. Literature searches were performed on Scopus (2014-2021), ProQuest and Ovid MEDLINE databases (2014-2021). Search terms included ‘heart failure’ or ‘cardiomyopathy’ and ‘machine learning’, ‘data analytics’, ‘data mining’ or ‘data science’. 81 out of 1688 articles were included in the review. The majority of studies were retrospective cohort studies. The median size of the patient cohort across all studies was 1944 (min 46, max 93260). The largest patient samples were used in readmission prediction models with the median sample size of 5676 (min. 380, max. 93260). Machine learning methods focused on common HF problems: detection of HF from available dataset, prediction of hospital readmission following index hospitalization, mortality prediction, classification and clustering of HF cohorts into subgroups with distinctive features and response to HF treatment. The most common ML methods used were logistic regression, decision trees, random forest and support vector machines. Information on validation of models was scarce. Based on the authors’ affiliations, there was a median 3:1 ratio between IT specialists and clinicians. Over half of studies were co-authored by a collaboration of medical and IT specialists. Approximately 25% of papers were authored solely by IT specialists who did not seek clinical input in data interpretation. The application of ML to datasets, in particular clustering methods, enabled the development of classification models assisting in testing the outcomes of patients with HF. There is, however, a tendency to over-claim the potential usefulness of ML models for clinical practice. The next body of work that is required for this research discipline is the design of randomised controlled trials (RCTs) with the use of ML in an intervention arm in order to prospectively validate these algorithms for real-world clinical utility.
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Lv, Haichen, Xiaolei Yang, Bingyi Wang, Shaobo Wang, Xiaoyan Du, Qian Tan, Zhujing Hao, Ying Liu, Jun Yan, and Yunlong Xia. "Machine Learning–Driven Models to Predict Prognostic Outcomes in Patients Hospitalized With Heart Failure Using Electronic Health Records: Retrospective Study." Journal of Medical Internet Research 23, no. 4 (April 19, 2021): e24996. http://dx.doi.org/10.2196/24996.
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Background With the prevalence of cardiovascular diseases increasing worldwide, early prediction and accurate assessment of heart failure (HF) risk are crucial to meet the clinical demand. Objective Our study objective was to develop machine learning (ML) models based on real-world electronic health records to predict 1-year in-hospital mortality, use of positive inotropic agents, and 1-year all-cause readmission rate. Methods For this single-center study, we recruited patients with newly diagnosed HF hospitalized between December 2010 and August 2018 at the First Affiliated Hospital of Dalian Medical University (Liaoning Province, China). The models were constructed for a population set (90:10 split of data set into training and test sets) using 79 variables during the first hospitalization. Logistic regression, support vector machine, artificial neural network, random forest, and extreme gradient boosting models were investigated for outcome predictions. Results Of the 13,602 patients with HF enrolled in the study, 537 (3.95%) died within 1 year and 2779 patients (20.43%) had a history of use of positive inotropic agents. ML algorithms improved the performance of predictive models for 1-year in-hospital mortality (areas under the curve [AUCs] 0.92-1.00), use of positive inotropic medication (AUCs 0.85-0.96), and 1-year readmission rates (AUCs 0.63-0.96). A decision tree of mortality risk was created and stratified by single variables at levels of high-sensitivity cardiac troponin I (<0.068 μg/L), followed by percentage of lymphocytes (<14.688%) and neutrophil count (4.870×109/L). Conclusions ML techniques based on a large scale of clinical variables can improve outcome predictions for patients with HF. The mortality decision tree may contribute to guiding better clinical risk assessment and decision making.
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Medic, Goran, Melodi Kosaner Kließ, Louis Atallah, Jochen Weichert, Saswat Panda, Maarten Postma, and Amer EL-Kerdi. "Evidence-based Clinical Decision Support Systems for the prediction and detection of three disease states in critical care: A systematic literature review." F1000Research 8 (October 8, 2019): 1728. http://dx.doi.org/10.12688/f1000research.20498.1.
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Background: Clinical decision support (CDS) systems have emerged as tools providing intelligent decision making to address challenges of critical care. CDS systems can be based on existing guidelines or best practices; and can also utilize machine learning to provide a diagnosis, recommendation, or therapy course. Methods: This research aimed to identify evidence-based study designs and outcome measures to determine the clinical effectiveness of clinical decision support systems in the detection and prediction of hemodynamic instability, respiratory distress, and infection within critical care settings. PubMed, ClinicalTrials.gov and Cochrane Database of Systematic Reviews were systematically searched to identify primary research published in English between 2013 and 2018. Studies conducted in the USA, Canada, UK, Germany and France with more than 10 participants per arm were included. Results: In studies on hemodynamic instability, the prediction and management of septic shock were the most researched topics followed by the early prediction of heart failure. For respiratory distress, the most popular topics were pneumonia detection and prediction followed by pulmonary embolisms. Given the importance of imaging and clinical notes, this area combined Machine Learning with image analysis and natural language processing. In studies on infection, the most researched areas were the detection, prediction, and management of sepsis, surgical site infections, as well as acute kidney injury. Overall, a variety of Machine Learning algorithms were utilized frequently, particularly support vector machines, boosting techniques, random forest classifiers and neural networks. Sensitivity, specificity, and ROC AUC were the most frequently reported performance measures. Conclusion: This review showed an increasing use of Machine Learning for CDS in all three areas. Large datasets are required for training these algorithms; making it imperative to appropriately address, challenges such as class imbalance, correct labelling of data and missing data. Recommendations are formulated for the development and successful adoption of CDS systems.
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Medic, Goran, Melodi Kosaner Kließ, Louis Atallah, Jochen Weichert, Saswat Panda, Maarten Postma, and Amer EL-Kerdi. "Evidence-based Clinical Decision Support Systems for the prediction and detection of three disease states in critical care: A systematic literature review." F1000Research 8 (November 27, 2019): 1728. http://dx.doi.org/10.12688/f1000research.20498.2.
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Background: Clinical decision support (CDS) systems have emerged as tools providing intelligent decision making to address challenges of critical care. CDS systems can be based on existing guidelines or best practices; and can also utilize machine learning to provide a diagnosis, recommendation, or therapy course. Methods: This research aimed to identify evidence-based study designs and outcome measures to determine the clinical effectiveness of clinical decision support systems in the detection and prediction of hemodynamic instability, respiratory distress, and infection within critical care settings. PubMed, ClinicalTrials.gov and Cochrane Database of Systematic Reviews were systematically searched to identify primary research published in English between 2013 and 2018. Studies conducted in the USA, Canada, UK, Germany and France with more than 10 participants per arm were included. Results: In studies on hemodynamic instability, the prediction and management of septic shock were the most researched topics followed by the early prediction of heart failure. For respiratory distress, the most popular topics were pneumonia detection and prediction followed by pulmonary embolisms. Given the importance of imaging and clinical notes, this area combined Machine Learning with image analysis and natural language processing. In studies on infection, the most researched areas were the detection, prediction, and management of sepsis, surgical site infections, as well as acute kidney injury. Overall, a variety of Machine Learning algorithms were utilized frequently, particularly support vector machines, boosting techniques, random forest classifiers and neural networks. Sensitivity, specificity, and ROC AUC were the most frequently reported performance measures. Conclusion: This review showed an increasing use of Machine Learning for CDS in all three areas. Large datasets are required for training these algorithms; making it imperative to appropriately address, challenges such as class imbalance, correct labelling of data and missing data. Recommendations are formulated for the development and successful adoption of CDS systems.
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Assegie, Tsehay Admassu, Vadivel Elanangai, Josephin Shermila Paulraj, Mani Velmurugan, and Daya Florance Devesan. "Evaluation of feature scaling for improving the performance of supervised learning methods." Bulletin of Electrical Engineering and Informatics 12, no. 3 (June 1, 2023): 1833–38. http://dx.doi.org/10.11591/eei.v12i3.5170.
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This article evaluates the performance of the support vector machine (SVM), decision tree (DT), and random forest (RF) on the dataset that contains the medical records of 299 patients with heart failure (HF) collected at the Faisalabad Institute of Cardiology and the Allied hospital in Pakistan. The dataset contains 13 descriptive features of physical, clinical, and lifestyle information. The study compared the performance of three classification algorithms employing pre-processing techniques such as min-max scaling, and principal component analysis (PCA). The simulation result shows that the performance of the DT, and RF decreased with dimensionality reduction while the SVM improved with dimensionality reduction. The SVM achieved 84.44%. Thus, feature scaling improves the performance of the SVM. The RF performs at 82.22%, the DT at 81.11%, and the SVM shows an improvement of 1.64% with scaled features, compared to the original dataset.
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Alimadadi, Ahmad, Ishan Manandhar, Sachin Aryal, Patricia B. Munroe, Bina Joe, and Xi Cheng. "Machine learning-based classification and diagnosis of clinical cardiomyopathies." Physiological Genomics 52, no. 9 (September 1, 2020): 391–400. http://dx.doi.org/10.1152/physiolgenomics.00063.2020.
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Dilated cardiomyopathy (DCM) and ischemic cardiomyopathy (ICM) are two common types of cardiomyopathies leading to heart failure. Accurate diagnostic classification of different types of cardiomyopathies is critical for precision medicine in clinical practice. In this study, we hypothesized that machine learning (ML) can be used as a novel diagnostic approach to analyze cardiac transcriptomic data for classifying clinical cardiomyopathies. RNA-Seq data of human left ventricle tissues were collected from 41 DCM patients, 47 ICM patients, and 49 nonfailure controls (NF) and tested using five ML algorithms: support vector machine with radial kernel (svmRadial), neural networks with principal component analysis (pcaNNet), decision tree (DT), elastic net (ENet), and random forest (RF). Initial ML classifications achieved ~93% accuracy (svmRadial) for NF vs. DCM, ~82% accuracy (RF) for NF vs. ICM, and ~80% accuracy (ENet and svmRadial) for DCM vs. ICM. Next, 50 highly contributing genes (HCGs) for classifying NF and DCM, 68 HCGs for classifying NF and ICM, and 59 HCGs for classifying DCM and ICM were selected for retraining ML models. Impressively, the retrained models achieved ~90% accuracy (RF) for NF vs. DCM, ~90% accuracy (pcaNNet) for NF vs. ICM, and ~85% accuracy (pcaNNet and RF) for DCM vs. ICM. Pathway analyses further confirmed the involvement of those selected HCGs in cardiac dysfunctions such as cardiomyopathies, cardiac hypertrophies, and fibrosis. Overall, our study demonstrates the promising potential of using artificial intelligence via ML modeling as a novel approach to achieve a greater level of precision in diagnosing different types of cardiomyopathies.
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COŞKUN, Cevdet, and Fatma KUNCAN. "EVALUATION OF PERFORMANCE OF CLASSIFICATION ALGORITHMS IN PREDICTION OF HEART FAILURE DISEASE." Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi 25, no. 4 (December 3, 2022): 622–32. http://dx.doi.org/10.17780/ksujes.1144570.
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Success rates and performances of Gaussian Naive Bayes, Support Vector Machines, Linear Discriminant Analysis, Decision Tree and Random Forest classifier algorithms from machine learning methods were evaluated using the Heart Failure Prediction dataset. Label encoder method was used primarily in data preprocessing techniques on the data set. Catalog data (5 pieces) in the data set have been converted into numerical data. In addition, it was observed that there were negative values in the data in a field and this situation was converted to values in the range of 0 - 1 with min-max conversion methods. After the pre-processing, analyzes were made with classification algorithms. As a result of these analyzes, a success rate of 90.76% was achieved with the random forest algorithm, which is an ensemble classifier. In the study, 80% of the data was used for training and 20% for testing. Of the 184 data used for the test, 102 of them were patients with heart failure and 72 of them were from those without the disease. The success of the random forest algorithm in estimating those with heart failure disease was 93.1% (95 observations), and the success in predicting those without the disease was 87.8% (72 observations).
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Mitchell, Joshua D., Daniel J. Lenihan, Casey Reed, Ahsan Huda, Kim Nolen, Marianna Bruno, and Thomas Kannampallil. "Implementing a Machine-Learning-Adapted Algorithm to Identify Possible Transthyretin Amyloid Cardiomyopathy at an Academic Medical Center." Clinical Medicine Insights: Cardiology 16 (January 2022): 117954682211336. http://dx.doi.org/10.1177/11795468221133608.
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Background: Wild-type transthyretin amyloid cardiomyopathy (ATTR-CM) is a frequently under-recognized cause of heart failure (HF) in older patients. To improve identification of patients at risk for the disease, we initiated a pilot program in which 9 cardiac/non-cardiac phenotypes and 20 high-performing phenotype combinations predictive of wild-type ATTR-CM were operationalized in electronic health record (EHR) configurations at a large academic medical center. Methods: Inclusion criteria were age >50 years and HF; exclusion criteria were end-stage renal disease and prior amyloidosis diagnoses. The different Epic EHR configurations investigated were a clinical decision support tool (Best Practice Advisory) and operational/analytical reports (Clarity™, Reporting Workbench™, and SlicerDicer); the different data sources employed were problem list, visit diagnosis, medical history, and billing transactions. Results: With Clarity, among 45 051 patients with HF, 4006 patients (8.9%) had ⩾1 phenotype combination associated with increased risk of wild-type ATTR-CM. Across all data sources, 2 phenotypes (cardiomegaly; osteoarthrosis) and 2 combinations (carpal tunnel syndrome + HF; atrial fibrillation + heart block + cardiomegaly + osteoarthrosis) generated the highest proportions of patients for wild-type ATTR-CM screening. Conclusion: All EHR configurations tested were capable of operationalizing phenotypes or phenotype combinations to identify at-risk patients; the Clarity report was the most comprehensive.
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Melgarejo-Meseguer, Francisco-Manuel, Francisco-Javier Gimeno-Blanes, María-Eladia Salar-Alcaraz, Juan-Ramón Gimeno-Blanes, Juan Martínez-Sánchez, Arcadi García-Alberola, and José Luis Rojo-Álvarez. "Electrocardiographic Fragmented Activity (II): A Machine Learning Approach to Detection." Applied Sciences 9, no. 17 (August 31, 2019): 3565. http://dx.doi.org/10.3390/app9173565.
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Hypertrophic cardiomyopathy, according to its prevalence, is a comparatively common disease related to the risk of suffering sudden cardiac death, heart failure and stroke. This illness is characterized by the excessive deposition of collagen among healthy myocardium cells. This situation, which is medically known as fibrosis, constitutes effective conduction obstacles in the myocardium electrical path, and when severe enough, it can be outlined as additional peaks or notches in the QRS, clinically entitled as fragmentation. Nowadays, the fragmentation detection is performed by visual inspection, but the fragmented QRS can be confused with the noise present in the electrocardiogram (ECG). On the other hand, fibrosis detection is performed by magnetic resonance imaging with late gadolinium enhancement, the main drawback of this technique being its cost in terms of time and money. In this work, we propose two automatic algorithms, one for fragmented QRS detection and another for fibrosis detection. For this purpose, we used four different databases, including the subrogated database described in the companion paper and incorporating three additional ones, one compounded by more accurate subrogated ECG signals and two compounded by real and affected subjects as labeled by expert clinicians. The first real-world database contains QRS fragmented records and the second one contains records with fibrosis and both were recorded in Hospital Clínico Universitario Virgen de la Arrixaca (Spain). To deeply analyze the scope of these datasets, we benchmarked several classifiers such as Neural Networks, Support Vector Machines (SVM), Decision Trees and Gaussian Naïve Bayes (NB). For the fragmentation dataset, the best results were 0.94 sensitivity, 0.88 specificity, 0.89 positive predictive value, 0.93 negative predictive value and 0.91 accuracy when using SVM with Gaussian kernel. For the fibrosis databases, more limited accuracy was reached, with 0.47 sensitivity, 0.91 specificity, 0.82 predictive positive value, 0.66 negative predictive value and 0.70 accuracy when using Gaussian NB. Nevertheless, this is the first time that fibrosis detection is attempted automatically from ECG postprocessing, paving the way towards improved algorithms and methods for it. Therefore, we can conclude that the proposed techniques could offer a valuable tool to clinicians for both fragmentation and fibrosis diagnoses support.
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Ullah, Zahid, Farrukh Saleem, Mona Jamjoom, Bahjat Fakieh, Faris Kateb, Abdullah Marish Ali, and Babar Shah. "Detecting High-Risk Factors and Early Diagnosis of Diabetes Using Machine Learning Methods." Computational Intelligence and Neuroscience 2022 (September 29, 2022): 1–10. http://dx.doi.org/10.1155/2022/2557795.
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Diabetes is a chronic disease that can cause several forms of chronic damage to the human body, including heart problems, kidney failure, depression, eye damage, and nerve damage. There are several risk factors involved in causing this disease, with some of the most common being obesity, age, insulin resistance, and hypertension. Therefore, early detection of these risk factors is vital in helping patients reverse diabetes from the early stage to live healthy lives. Machine learning (ML) is a useful tool that can easily detect diabetes from several risk factors and, based on the findings, provide a decision-based model that can help in diagnosing the disease. This study aims to detect the risk factors of diabetes using ML methods and to provide a decision support system for medical practitioners that can help them in diagnosing diabetes. Moreover, besides various other preprocessing steps, this study has used the synthetic minority over-sampling technique integrated with the edited nearest neighbor (SMOTE-ENN) method for balancing the BRFSS dataset. The SMOTE-ENN is a more powerful method than the individual SMOTE method. Several ML methods were applied to the processed BRFSS dataset and built prediction models for detecting the risk factors that can help in diagnosing diabetes patients in the early stage. The prediction models were evaluated using various measures that show the high performance of the models. The experimental results show the reliability of the proposed models, demonstrating that k-nearest neighbor (KNN) outperformed other methods with an accuracy of 98.38%, sensitivity, specificity, and ROC/AUC score of 98%. Moreover, compared with the existing state-of-the-art methods, the results confirm the efficacy of the proposed models in terms of accuracy and other evaluation measures. The use of SMOTE-ENN is more beneficial for balancing the dataset to build more accurate prediction models. This was the main reason it was possible to achieve models more accurate than the existing ones.
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Bollepalli, Sandeep Chandra, Ashish Kumar Sahani, Naved Aslam, Bishav Mohan, Kanchan Kulkarni, Abhishek Goyal, Bhupinder Singh, et al. "An Optimized Machine Learning Model Accurately Predicts In-Hospital Outcomes at Admission to a Cardiac Unit." Diagnostics 12, no. 2 (January 19, 2022): 241. http://dx.doi.org/10.3390/diagnostics12020241.
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Risk stratification at the time of hospital admission is of paramount significance in triaging the patients and providing timely care. In the present study, we aim at predicting multiple clinical outcomes using the data recorded during admission to a cardiac care unit via an optimized machine learning method. This study involves a total of 11,498 patients admitted to a cardiac care unit over two years. Patient demographics, admission type (emergency or outpatient), patient history, lab tests, and comorbidities were used to predict various outcomes. We employed a fully connected neural network architecture and optimized the models for various subsets of input features. Using 10-fold cross-validation, our optimized machine learning model predicted mortality with a mean area under the receiver operating characteristic curve (AUC) of 0.967 (95% confidence interval (CI): 0.963–0.972), heart failure AUC of 0.838 (CI: 0.825–0.851), ST-segment elevation myocardial infarction AUC of 0.832 (CI: 0.821–0.842), pulmonary embolism AUC of 0.802 (CI: 0.764–0.84), and estimated the duration of stay (DOS) with a mean absolute error of 2.543 days (CI: 2.499–2.586) of data with a mean and median DOS of 6.35 and 5.0 days, respectively. Further, we objectively quantified the importance of each feature and its correlation with the clinical assessment of the corresponding outcome. The proposed method accurately predicts various cardiac outcomes and can be used as a clinical decision support system to provide timely care and optimize hospital resources.
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Krishnamoorthi, Raja, Shubham Joshi, Hatim Z. Almarzouki, Piyush Kumar Shukla, Ali Rizwan, C. Kalpana, and Basant Tiwari. "A Novel Diabetes Healthcare Disease Prediction Framework Using Machine Learning Techniques." Journal of Healthcare Engineering 2022 (January 11, 2022): 1–10. http://dx.doi.org/10.1155/2022/1684017.
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Diabetes is a chronic disease that continues to be a significant and global concern since it affects the entire population’s health. It is a metabolic disorder that leads to high blood sugar levels and many other problems such as stroke, kidney failure, and heart and nerve problems. Several researchers have attempted to construct an accurate diabetes prediction model over the years. However, this subject still faces significant open research issues due to a lack of appropriate data sets and prediction approaches, which pushes researchers to use big data analytics and machine learning (ML)-based methods. Applying four different machine learning methods, the research tries to overcome the problems and investigate healthcare predictive analytics. The study’s primary goal was to see how big data analytics and machine learning-based techniques may be used in diabetes. The examination of the results shows that the suggested ML-based framework may achieve a score of 86. Health experts and other stakeholders are working to develop categorization models that will aid in the prediction of diabetes and the formulation of preventative initiatives. The authors perform a review of the literature on machine models and suggest an intelligent framework for diabetes prediction based on their findings. Machine learning models are critically examined, and an intelligent machine learning-based architecture for diabetes prediction is proposed and evaluated by the authors. In this study, the authors utilize our framework to develop and assess decision tree (DT)-based random forest (RF) and support vector machine (SVM) learning models for diabetes prediction, which are the most widely used techniques in the literature at the time of writing. It is proposed in this study that a unique intelligent diabetes mellitus prediction framework (IDMPF) is developed using machine learning. According to the framework, it was developed after conducting a rigorous review of existing prediction models in the literature and examining their applicability to diabetes. Using the framework, the authors describe the training procedures, model assessment strategies, and issues associated with diabetes prediction, as well as solutions they provide. The findings of this study may be utilized by health professionals, stakeholders, students, and researchers who are involved in diabetes prediction research and development. The proposed work gives 83% accuracy with the minimum error rate.
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Duong, Son Q., Le Zheng, Minjie Xia, Bo Jin, Modi Liu, Zhen Li, Shiying Hao, et al. "Identification of patients at risk of new onset heart failure: Utilizing a large statewide health information exchange to train and validate a risk prediction model." PLOS ONE 16, no. 12 (December 10, 2021): e0260885. http://dx.doi.org/10.1371/journal.pone.0260885.
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Background New-onset heart failure (HF) is associated with poor prognosis and high healthcare utilization. Early identification of patients at increased risk incident-HF may allow for focused allocation of preventative care resources. Health information exchange (HIE) data span the entire spectrum of clinical care, but there are no HIE-based clinical decision support tools for diagnosis of incident-HF. We applied machine-learning methods to model the one-year risk of incident-HF from the Maine statewide-HIE. Methods and results We included subjects aged ≥ 40 years without prior HF ICD9/10 codes during a three-year period from 2015 to 2018, and incident-HF defined as assignment of two outpatient or one inpatient code in a year. A tree-boosting algorithm was used to model the probability of incident-HF in year two from data collected in year one, and then validated in year three. 5,668 of 521,347 patients (1.09%) developed incident-HF in the validation cohort. In the validation cohort, the model c-statistic was 0.824 and at a clinically predetermined risk threshold, 10% of patients identified by the model developed incident-HF and 29% of all incident-HF cases in the state of Maine were identified. Conclusions Utilizing machine learning modeling techniques on passively collected clinical HIE data, we developed and validated an incident-HF prediction tool that performs on par with other models that require proactively collected clinical data. Our algorithm could be integrated into other HIEs to leverage the EMR resources to provide individuals, systems, and payors with a risk stratification tool to allow for targeted resource allocation to reduce incident-HF disease burden on individuals and health care systems.
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Kor, Chew-Teng, Yi-Rong Li, Pei-Ru Lin, Sheng-Hao Lin, Bing-Yen Wang, and Ching-Hsiung Lin. "Explainable Machine Learning Model for Predicting First-Time Acute Exacerbation in Patients with Chronic Obstructive Pulmonary Disease." Journal of Personalized Medicine 12, no. 2 (February 7, 2022): 228. http://dx.doi.org/10.3390/jpm12020228.
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Background: The study developed accurate explainable machine learning (ML) models for predicting first-time acute exacerbation of chronic obstructive pulmonary disease (COPD, AECOPD) at an individual level. Methods: We conducted a retrospective case–control study. A total of 606 patients with COPD were screened for eligibility using registry data from the COPD Pay-for-Performance Program (COPD P4P program) database at Changhua Christian Hospital between January 2017 and December 2019. Recursive feature elimination technology was used to select the optimal subset of features for predicting the occurrence of AECOPD. We developed four ML models to predict first-time AECOPD, and the highest-performing model was applied. Finally, an explainable approach based on ML and the SHapley Additive exPlanations (SHAP) and a local explanation method were used to evaluate the risk of AECOPD and to generate individual explanations of the model’s decisions. Results: The gradient boosting machine (GBM) and support vector machine (SVM) models exhibited superior discrimination ability (area under curve [AUC] = 0.833 [95% confidence interval (CI) 0.745–0.921] and AUC = 0.836 [95% CI 0.757–0.915], respectively). The decision curve analysis indicated that the GBM model exhibited a higher net benefit in distinguishing patients at high risk for AECOPD when the threshold probability was <0.55. The COPD Assessment Test (CAT) and the symptom of wheezing were the two most important features and exhibited the highest SHAP values, followed by monocyte count and white blood cell (WBC) count, coughing, red blood cell (RBC) count, breathing rate, oral long-acting bronchodilator use, chronic pulmonary disease (CPD), systolic blood pressure (SBP), and others. Higher CAT score; monocyte, WBC, and RBC counts; BMI; diastolic blood pressure (DBP); neutrophil-to-lymphocyte ratio; and eosinophil and lymphocyte counts were associated with AECOPD. The presence of symptoms (wheezing, dyspnea, coughing), chronic disease (CPD, congestive heart failure [CHF], sleep disorders, and pneumonia), and use of COPD medications (triple-therapy long-acting bronchodilators, short-acting bronchodilators, oral long-acting bronchodilators, and antibiotics) were also positively associated with AECOPD. A high breathing rate, heart rate, or systolic blood pressure and methylxanthine use were negatively correlated with AECOPD. Conclusions: The ML model was able to accurately assess the risk of AECOPD. The ML model combined with SHAP and the local explanation method were able to provide interpretable and visual explanations of individualized risk predictions, which may assist clinical physicians in understanding the effects of key features in the model and the model’s decision-making process.
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Djulbegovic, Benjamin, Jennifer Berano Teh, Lennie Wong, Iztok Hozo, and Saro H. Armenian. "Diagnostic Predictive Model for Diagnosis of Heart Failure after Hematopoietic Cell Transplantation (HCT): Comparison of Traditional Statistical with Machine Learning Modeling." Blood 134, Supplement_1 (November 13, 2019): 5799. http://dx.doi.org/10.1182/blood-2019-130764.
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Background: Therapy-related heart failure (HF) is a leading cause of morbidity and mortality in patients who undergo successful HCT for hematological malignancies. To eventually help improve management of HF, timely and accurate diagnosis of HF is crucial. Currently, no established method for diagnosis of HF exist. One approach to help improve diagnosis and management of HF is to use predictive modeling to assess the likelihood of HF, using key predictors known to be associated with HF. Such models can, in turn, be used for bedside management, including implementation of early screening approaches. That said, many techniques for predictive modeling exist. Currently, it is not known if Artificial Intelligence machine learning (ML) approaches are superior to standard statistical techniques for the development of predictive models for clinical practice. Here we present a comparative analysis of traditional multivariable models with ML learning predictive modeling in an attempt to identify the best predictive model for diagnosis of HF after HCT. Methods: At City of Hope, we have established a large prospective cohort (>12,000 patients) of HCT survivors (HCT survivorship registry). This registry is dynamic, and interfaces with other registries and databases (e.g., electronically indexed inpatient and outpatient medical records, national death index [NDI]). We utilized natural language processing (NLP) to extract 13 key demographics and clinical data that are known to be associated with HF. For the purposes of this project, we extracted data from 1,834 patients (~15% sample) who underwent HCT between 1994 to 2004 to allow adequate follow-up for the development of HF. We fit and compared 6 models [standard logistic regression (glm), FFT (fast-and-frugal tree) decision model and four ML models: CART (classification and regression trees), SVM (support vector machine), NN (neural network) and RF (random forest)]. Data were randomly split (50:50) into training and validation samples; the ultimate assessment of the best algorithm was based on its performance (in terms of calibration and discrimination) in the validation sample. DeLong test was used to test for statistical differences in discrimination [i.e. area under the curve (AUC)] among the models. Results: The accuracy of NLP was consistently >95%. Only the standard logistic regression model LR (glm) resulted in a well-calibrated model (Hosmer-Lemeshow goodness of fitness test: p=0.104); all other models were miscalibrated. The standard glm model also had best discrimination properties (AUC=0.704 in training and 0.619 in validation set). CART performed the worst (AUC=0.5). Other ML models (RF, NN, and SVM) also showed modest discriminatory characteristics (AUCs of 0.547, 0.573, and 0.619, respectively). DeLong test indicated that all models outperformed CART model (at nominal p<0.05 ), but were statistically indistinguishable among each other (see Figure). Power of analysis was borderline sufficient for the glm model and very limited for ML models. Conclusions: None of tested models showed optimal performance characteristics for their use in clinical practice. ML models performed even worse than standard logistic models; given increasing use of ML models in medicine, we caution against the use of these models without adequate comparative testing. Figure Disclosures No relevant conflicts of interest to declare.
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Liu, K. E., C. L. Lo, and Y. H. Hu. "Improvement of Adequate Use of Warfarin for the Elderly Using Decision Tree-based Approaches." Methods of Information in Medicine 53, no. 01 (2014): 47–53. http://dx.doi.org/10.3414/me13-01-0027.
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SummaryObjectives: Due to the narrow therapeutic range and high drug-to-drug interactions (DDIs), improving the adequate use of warfarin for the elderly is crucial in clinical practice. This study examines whether the effectiveness of using warfarin among elderly inpatients can be improved when machine learning techniques and data from the laboratory information system are incorporated.Methods: Having employed 288 validated clinical cases in the DDI group and 89 cases in the non-DDI group, we evaluate the prediction performance of seven classification techniques, with and without an Adaptive Boosting (AdaBoost) algorithm. Measures including accuracy, sensitivity, specificity and area under the curve are used to evaluate model performance.Results: Decision tree-based classifiers outperform other investigated classifiers in all evaluation measures. The classifiers supplemented with AdaBoost can generally improve the performance. In addition, weight, congestive heart failure, and gender are among the top three critical variables affecting prediction accuracy for the non-DDI group, while age, ALT, and warfarin doses are the most influential factors for the DDI group.Conclusion: Medical decision support systems incorporating decision tree-based approaches improve predicting performance and thus may serve as a supplementary tool in clinical practice. Information from laboratory tests and inpatients’ history should not be ignored because related variables are shown to be decisive in our prediction models, especially when the DDIs exist.
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Khan, Asfandyar, Abdullah Khan, Muhammad Muntazir Khan, Kamran Farid, Muhammad Mansoor Alam, and Mazliham Bin Mohd Su’ud. "Cardiovascular and Diabetes Diseases Classification Using Ensemble Stacking Classifiers with SVM as a Meta Classifier." Diagnostics 12, no. 11 (October 26, 2022): 2595. http://dx.doi.org/10.3390/diagnostics12112595.
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Cardiovascular disease includes coronary artery diseases (CAD), which include angina and myocardial infarction (commonly known as a heart attack), and coronary heart diseases (CHD), which are marked by the buildup of a waxy material called plaque inside the coronary arteries. Heart attacks are still the main cause of death worldwide, and if not treated right they have the potential to cause major health problems, such as diabetes. If ignored, diabetes can result in a variety of health problems, including heart disease, stroke, blindness, and kidney failure. Machine learning methods can be used to identify and diagnose diabetes and other illnesses. Diabetes and cardiovascular disease both can be diagnosed using several classifier types. Naive Bayes, K-Nearest neighbor (KNN), linear regression, decision trees (DT), and support vector machines (SVM) were among the classifiers employed, although all of these models had poor accuracy. Therefore, due to a lack of significant effort and poor accuracy, new research is required to diagnose diabetes and cardiovascular disease. This study developed an ensemble approach called “Stacking Classifier” in order to improve the performance of integrated flexible individual classifiers and decrease the likelihood of misclassifying a single instance. Naive Bayes, KNN, Linear Discriminant Analysis (LDA), and Decision Tree (DT) are just a few of the classifiers used in this study. As a meta-classifier, Random Forest and SVM are used. The suggested stacking classifier obtains a superior accuracy of 0.9735 percent when compared to current models for diagnosing diabetes, such as Naive Bayes, KNN, DT, and LDA, which are 0.7646 percent, 0.7460 percent, 0.7857 percent, and 0.7735 percent, respectively. Furthermore, for cardiovascular disease, when compared to current models such as KNN, NB, DT, LDA, and SVM, which are 0.8377 percent, 0.8256 percent, 0.8426 percent, 0.8523 percent, and 0.8472 percent, respectively, the suggested stacking classifier performed better and obtained a higher accuracy of 0.8871 percent.
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Luštrek, Mitja, Marko Bohanec, Carlos Cavero Barca, Maria Costanza Ciancarelli, Els Clays, Amos Adeyemo Dawodu, Jan Derboven, et al. "A Personal Health System for Self-Management of Congestive Heart Failure (HeartMan): Development, Technical Evaluation, and Proof-of-Concept Randomized Controlled Trial." JMIR Medical Informatics 9, no. 3 (March 5, 2021): e24501. http://dx.doi.org/10.2196/24501.
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Background Congestive heart failure (CHF) is a disease that requires complex management involving multiple medications, exercise, and lifestyle changes. It mainly affects older patients with depression and anxiety, who commonly find management difficult. Existing mobile apps supporting the self-management of CHF have limited features and are inadequately validated. Objective The HeartMan project aims to develop a personal health system that would comprehensively address CHF self-management by using sensing devices and artificial intelligence methods. This paper presents the design of the system and reports on the accuracy of its patient-monitoring methods, overall effectiveness, and patient perceptions. Methods A mobile app was developed as the core of the HeartMan system, and the app was connected to a custom wristband and cloud services. The system features machine learning methods for patient monitoring: continuous blood pressure (BP) estimation, physical activity monitoring, and psychological profile recognition. These methods feed a decision support system that provides recommendations on physical health and psychological support. The system was designed using a human-centered methodology involving the patients throughout development. It was evaluated in a proof-of-concept trial with 56 patients. Results Fairly high accuracy of the patient-monitoring methods was observed. The mean absolute error of BP estimation was 9.0 mm Hg for systolic BP and 7.0 mm Hg for diastolic BP. The accuracy of psychological profile detection was 88.6%. The F-measure for physical activity recognition was 71%. The proof-of-concept clinical trial in 56 patients showed that the HeartMan system significantly improved self-care behavior (P=.02), whereas depression and anxiety rates were significantly reduced (P<.001), as were perceived sexual problems (P=.01). According to the Unified Theory of Acceptance and Use of Technology questionnaire, a positive attitude toward HeartMan was seen among end users, resulting in increased awareness, self-monitoring, and empowerment. Conclusions The HeartMan project combined a range of advanced technologies with human-centered design to develop a complex system that was shown to help patients with CHF. More psychological than physical benefits were observed. Trial Registration ClinicalTrials.gov NCT03497871; https://clinicaltrials.gov/ct2/history/NCT03497871. International Registered Report Identifier (IRRID) RR2-10.1186/s12872-018-0921-2
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Hamzah, Nurul Farhana, Nazri Mohd Nawi, and Abdulkareem A. Hezam. "The Analysis Performance of Heart Failure Classification by Using Machine Learning Techniques." Journal of Soft Computing and Data Mining 2, no. 2 (October 15, 2021). http://dx.doi.org/10.30880/jscdm.2021.02.02.009.
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Heart failure means that the heart is not pumping well as normal as it should be. A congestive heart failure is a form of heart failure that involves seeking timely medical care, although the two terms are sometimes used interchangeably. Heart failure happens when the heart muscle does not pump blood as well as it can, often referred to as congestive heart failure. Some disorders, such as heart's narrowed arteries (coronary artery disease) or high blood pressure, eventually make the heart too weak or rigid to fill and pump effectively. Early detection of heart failure by using data mining techniques has gained popularity among researchers. This research uses some classification techniques for heart failure classification from medical data. This research analyzed the performance of some classification algorithms, namely Support Vector Machine (SVM), Decision Forest (DF), and Boosted Decision Tree (BDT), to classify accurately heart failure risk data as input. The best algorithm among the three is discovered for heart failure classification at the end of this research.
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Mpanya, Dineo, Turgay Celik, Eric Klug, and Hopewell Ntsinjana. "Predicting in-hospital all-cause mortality in heart failure using machine learning." Frontiers in Cardiovascular Medicine 9 (January 11, 2023). http://dx.doi.org/10.3389/fcvm.2022.1032524.
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BackgroundThe age of onset and causes of heart failure differ between high-income and low-and-middle-income countries (LMIC). Heart failure patients in LMIC also experience a higher mortality rate. Innovative ways that can risk stratify heart failure patients in this region are needed. The aim of this study was to demonstrate the utility of machine learning in predicting all-cause mortality in heart failure patients hospitalised in a tertiary academic centre.MethodsSix supervised machine learning algorithms were trained to predict in-hospital all-cause mortality using data from 500 consecutive heart failure patients with a left ventricular ejection fraction (LVEF) less than 50%.ResultsThe mean age was 55.2 ± 16.8 years. There were 271 (54.2%) males, and the mean LVEF was 29 ± 9.2%. The median duration of hospitalisation was 7 days (interquartile range: 4–11), and it did not differ between patients discharged alive and those who died. After a prediction window of 4 years (interquartile range: 2–6), 84 (16.8%) patients died before discharge from the hospital. The area under the receiver operating characteristic curve was 0.82, 0.78, 0.77, 0.76, 0.75, and 0.62 for random forest, logistic regression, support vector machines (SVM), extreme gradient boosting, multilayer perceptron (MLP), and decision trees, and the accuracy during the test phase was 88, 87, 86, 82, 78, and 76% for random forest, MLP, SVM, extreme gradient boosting, decision trees, and logistic regression. The support vector machines were the best performing algorithm, and furosemide, beta-blockers, spironolactone, early diastolic murmur, and a parasternal heave had a positive coefficient with the target feature, whereas coronary artery disease, potassium, oedema grade, ischaemic cardiomyopathy, and right bundle branch block on electrocardiogram had negative coefficients.ConclusionDespite a small sample size, supervised machine learning algorithms successfully predicted all-cause mortality with modest accuracy. The SVM model will be externally validated using data from multiple cardiology centres in South Africa before developing a uniquely African risk prediction tool that can potentially transform heart failure management through precision medicine.
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"Machine Learning Based Diagnosis and Prediction System for Congestive Heart Failure." VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE 8, no. 10 (August 10, 2019): 1800–1804. http://dx.doi.org/10.35940/ijitee.j9190.0881019.
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Recently, heart failure has become one of the major Causes of death. By 2030, if it is not controlled the toll will rise to twenty three million. Cardiologist can predict the disease with 70 % accuracy. Considering the limitation of cardiologist, a system can be provided to them to predict the disease with more accuracy. Machine Learning is frequently used in to days world to support healthcare industry. ML provides new opportunity to analyze the data with more accuracy. It bridges the gap between medical science and technology. Decision tree is one of the best classification techniques of machine learning which will analyze the data and predict the disease with accuracy. The main objective of my dissertation work is to predict the disease and analyze the result. So in this research work the DT technique is used for the prediction of disease and it gave result with more accuracy on comparison to previous work. Hence this study proved that DT algorithm gives the result with more accuracy in less time of execution. This research work is a growing range of efficient tools to assist healthcare industry and medical professionals for the betterment of patients.
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"Cardiovascular Disease Recognition through Machine Learning Algorithms." International Journal of Engineering and Advanced Technology 9, no. 4 (April 30, 2020): 2109–15. http://dx.doi.org/10.35940/ijeat.d9149.049420.
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The heart is more important to the human body than any other circulatory organs. Its function is to provide and pump blood to other organs and brain. So it is very important to have a healthy heart but researches revealed the risk of heart failure increases every day starting from age 30. Many heart specialist can diagnose heart disease with their experience and skills. But some experts lacking the talent or knowledge to predict cardiovascular disease in the early stages, a small mistake can cost a patient’s life. Therefore, it is necessary to use specific methods and algorithmic tools to estimate the occurrence of cardiac disorders in the early stages. Different Algorithms for machine learning and data analysis are beneficial in predicting various diseases from patient’s data, managed by the Medical Center or hospitals. The data obtained may also help to assess the presence of the disease in the future. Heart Disease or Cardiac related issues can be analyzed by variety of machine learning techniques, Instance Artificial Neural Network, Decision Tree, Random forest, K-nearest neighbor, Naïve Bayes and Support Vector Machine. This study establishes a theoretical understanding of existing algorithms and provides a general understanding of existing work.
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Morrill, James, Klajdi Qirko, Jacob Kelly, Andrew Ambrosy, Botros Toro, Ted Smith, Nicholas Wysham, Marat Fudim, and Sumanth Swaminathan. "A Machine Learning Methodology for Identification and Triage of Heart Failure Exacerbations." Journal of Cardiovascular Translational Research, August 28, 2021. http://dx.doi.org/10.1007/s12265-021-10151-7.
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Abstract Inadequate at-home management and self-awareness of heart failure (HF) exacerbations are known to be leading causes of the greater than 1 million estimated HF-related hospitalizations in the USA alone. Most current at-home HF management protocols include paper guidelines or exploratory health applications that lack rigor and validation at the level of the individual patient. We report on a novel triage methodology that uses machine learning predictions for real-time detection and assessment of exacerbations. Medical specialist opinions on statistically and clinically comprehensive, simulated patient cases were used to train and validate prediction algorithms. Model performance was assessed by comparison to physician panel consensus in a representative, out-of-sample validation set of 100 vignettes. Algorithm prediction accuracy and safety indicators surpassed all individual specialists in identifying consensus opinion on existence/severity of exacerbations and appropriate treatment response. The algorithms also scored the highest sensitivity, specificity, and PPV when assessing the need for emergency care. Lay summary Here we develop a machine-learning approach for providing real-time decision support to adults diagnosed with congestive heart failure. The algorithm achieves higher exacerbation and triage classification performance than any individual physician when compared to physician consensus opinion. Graphical abstract
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Gutman, Rom, Doron Aronson, Oren Caspi, and Uri Shalit. "What drives performance in machine learning models for predicting heart failure outcome?" European Heart Journal - Digital Health, September 30, 2022. http://dx.doi.org/10.1093/ehjdh/ztac054.
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Abstract Introduction The development of acute heart failure (AHF) is a critical decision point in the natural history of the disease and carries a dismal prognosis. The lack of appropriate risk-stratification tools at hospital discharge of AHF patients significantly limits clinical ability to precisely tailor patient-specific therapeutic regimen at this pivotal juncture. Machine learning based strategies may improve risk stratification by incorporating analysis of high-dimensional patient data with multiple covariates and novel prediction methodologies. In the current study, we aimed at evaluating the drivers for success in prediction models and establishing an institute-tailored AI based prediction model for real-time decision support. Methods We used a cohort of all 10,868 patients AHF patients admitted to a tertiary hospital during a 12 years period. A total of 372 covariates were collected from admission to the end of the hospitalization. We assessed model performance across two axes: (1) type of prediction method and (2) type and number of covariates. The primary outcome was one-year survival from hospital discharge. For the model-type axis we experimented with seven different methods: Logistic Regression(LR) with either L1 or L2 regularization, Random Forest (RF), Cox proportional hazards model (Cox), XGBoost, a deep neural-net (NeuralNet) and an ensemble classifier of all the above methods. Results We were able to achieve a AUROC prediction accuracy of more than 80% with most prediction models including L1/L2-LR (80.4%/80.3%), Cox (80.2%), XGBoost (80.5%), NeuralNet (80.4%). Random Forest was inferior to other methods (78.8%) and the ensemble model was slightly superior (81.2%). The number of covariates was a significant modifier (p < 0.001) of prediction success, the use of multiplex-covariates preformed significantly better (AUROC 80.4% for L1-LR) compared with a set of known clinical covariates (AUROC 77.8%). Demographics followed by lab-tests and administrative data resulted in the largest gain in model performance. Conclusions The choice of the predictive modeling method is secondary to the multiplicity and type of covariates for predicting AHF prognosis. The application of a structured data pre-processing combined with the use of multiple-covariates results in an accurate, institute-tailored, risk prediction in AHF
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Doudesis, D., K. K. Lee, M. Anwar, F. Astengo, D. Newby, A. Japp, A. Tsanas, et al. "Machine learning to aid in the diagnosis of acute heart failure in the emergency department." European Heart Journal 42, Supplement_1 (October 1, 2021). http://dx.doi.org/10.1093/eurheartj/ehab724.1040.
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Abstract Background B-type natriuretic peptide (BNP) and mid-regional pro-atrial natriuretic peptide (MRproANP) testing are recommended to aid in the diagnosis of acute heart failure. However, the application of these biomarkers for optimal diagnostic performance is uncertain. Methods We performed a systematic review and harmonised individual patient-level data to evaluate the diagnostic performance of BNP and MRproANP for the diagnosis of acute heart failure using random-effects meta-analysis. We subsequently developed and externally validated a decision-support tool called CoDE-HF for both BNP and MRproANP that combines the natriuretic peptide concentrations with clinical variables using machine learning to report the probability of acute heart failure for an individual patient. Results Fourteen studies from 12 countries provided individual patient-level data in 8,493 patients for BNP and 3,847 patients for MRproANP, in whom, 48.3% (4,105/8,493) and 41.3% (1,611/3899) had an adjudicated diagnosis of acute heart failure, respectively. The negative and positive predictive values of guideline-recommended thresholds for BNP (100 pg/mL) and MR-proANP (120 pg/mL) were 93.6% (95% confidence interval 88.4–96.6%) and 68.8% (62.9–74.2%), and 95.6% (92.2–97.6%) and 64.8% (56.3–72.5%), respectively. However, we observed significant heterogeneity in the diagnostic performance across important patient subgroups (Figure 1). In the external validation cohort, CoDE-HF was well calibrated with excellent discrimination in those without prior acute heart failure for both BNP and MRproANP (area under the curve of 0.946 [0.933–0.958] and 0.943 [0.921–0.964], and Brier scores of 0.105 and 0.073, respectively). CoDE-HF performed consistently across all subgroups for both BNP and MRproANP, and identified 30% and 65.7% at low-probability (negative predictive value of 99.1% [98.8–99.3%] and 99.1% [98.8–99.4%]), and 30% and 17.3% at high-probability (positive predictive value of 91.3% [90.7–91.9%] and 70.0% [68.5–71.4%]) in those without prior heart failure, respectively (Figure 2). Conclusion In an international collaborative analysis, we observed that guideline-recommended thresholds for BNP and MRproANP to diagnose acute heart failure varied significantly across patient subgroups. A decision-support tool using machine learning to combine natriuretic peptides as a continuous measure and other clinical variables provides a more accurate and individualised approach. Funding Acknowledgement Type of funding sources: Other. Main funding source(s): Medical Research Council and British Heart Foundation Figure 1. NPV of BNP threshold (100 pg/mL)Figure 2. NPV of the CoDE-HF rule-out score
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N V, Suchetha, Asmitha Thulapule, Aishwarya Shetty S, G. J. Sahana, and Monisha B L. "A Hybrid Intelligent System Framework for Detecting and Predicting Risk of Cardiovascular Disease." Journal of Signal Processing 7, no. 2 (August 10, 2021). http://dx.doi.org/10.46610/josp.2021.v07i02.004.
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Number of deaths is increasing everyday especially due to heart disease in the present-day world. The cardiovascular diseases can appear due to family history, obesity, smoking etc. Prevention of death due to heart failure and proper treatment requires on time and accurate diagnosis of the disease. In this study we predict heart disease using a system that is based on machine learning using heart disease dataset. In this paper we try analysing and predicting heart diseases occurring by applying algorithms such as K-Nearest Neighbour, Naive Bayes, Support Vector Machine, Decision Tree, Random Forest, Logistic Regression, XG Boost. We have taken the dataset from UCI Machine Learning Repository. The algorithms results based on different factors like cholesterol, age, gender etc. The resulting performance provides efficiency in diagnosing the disease.
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Selek, Mustafa B., Bartu Yesilkaya, Saadet S. Egeli, and Yalcin Isler. "The effect of principal component analysis in the diagnosis of congestive heart failure via heart rate variability analysis." Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, August 7, 2021, 095441192110368. http://dx.doi.org/10.1177/09544119211036806.
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In this study, we investigated the effect of principal component analysis (PCA) in congestive heart failure (CHF) diagnosis using various machine learning algorithms from 5-min HRV data. The extracted 59 heart rate variability (HRV) features consist of statistical time-domain measures, frequency-domain measures (power spectral density estimations from Fourier transform and Lomb-Scargle methods), time-frequency HRV measures (Wavelet transform), and nonlinear HRV measures (Poincare plot, symbolic dynamics, detrended fluctuation analysis, and sample entropy). All these HRV features are the classifiers’ inputs. We repeated the study ten times using the first one to the first 10 principal components from PCA instead of all HRV features. Nine different classifiers, namely logistic regression, Naive Bayes, k-nearest neighbors, decision tree, AdaBoost, support vector machines, stochastic gradient descent, random forest, and artificial neuronal network (multilayer perceptron) are examined. The proposed study results in the 100% accuracy, 100% specificity, and 100% sensitivity after utilizing PCA (with the first eight principal components) using the Random Forest classifier where the maximum classifier performances are the 86% accuracy, 79% specificity, and 86% sensitivity before PCA. In conclusion, PCA is beneficial in the diagnosis of patients with CHF. In addition, we experienced the online Python-based visual machine learning tool, Orange, which can implement well-known machine learning algorithms.
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Penso, Marco, Sarah Solbiati, Sara Moccia, and Enrico G. Caiani. "Decision Support Systems in HF based on Deep Learning Technologies." Current Heart Failure Reports, February 10, 2022. http://dx.doi.org/10.1007/s11897-022-00540-7.
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Abstract Purpose of Review Application of deep learning (DL) is growing in the last years, especially in the healthcare domain. This review presents the current state of DL techniques applied to electronic health record structured data, physiological signals, and imaging modalities for the management of heart failure (HF), focusing in particular on diagnosis, prognosis, and re-hospitalization risk, to explore the level of maturity of DL in this field. Recent Findings DL allows a better integration of different data sources to distillate more accurate outcomes in HF patients, thus resulting in better performance when compared to conventional evaluation methods. While applications in image and signal processing for HF diagnosis have reached very high performance, the application of DL to electronic health records and its multisource data for prediction could still be improved, despite the already promising results. Summary Embracing the current big data era, DL can improve performance compared to conventional techniques and machine learning approaches. DL algorithms have potential to provide more efficient care and improve outcomes of HF patients, although further investigations are needed to overcome current limitations, including results generalizability and transparency and explicability of the evidences supporting the process.
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Rani, KM Jyoti. "Diabetes Prediction Using Machine Learning." International Journal of Scientific Research in Computer Science, Engineering and Information Technology, July 20, 2020, 294–305. http://dx.doi.org/10.32628/cseit206463.
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Diabetes is a chronic disease with the potential to cause a worldwide health care crisis. According to International Diabetes Federation 382 million people are living with diabetes across the whole world. By 2035, this will be doubled as 592 million. Diabetes is a disease caused due to the increase level of blood glucose. This high blood glucose produces the symptoms of frequent urination, increased thirst, and increased hunger. Diabetes is a one of the leading cause of blindness, kidney failure, amputations, heart failure and stroke. When we eat, our body turns food into sugars, or glucose. At that point, our pancreas is supposed to release insulin. Insulin serves as a key to open our cells, to allow the glucose to enter and allow us to use the glucose for energy. But with diabetes, this system does not work. Type 1 and type 2 diabetes are the most common forms of the disease, but there are also other kinds, such as gestational diabetes, which occurs during pregnancy, as well as other forms. Machine learning is an emerging scientific field in data science dealing with the ways in which machines learn from experience. The aim of this project is to develop a system which can perform early prediction of diabetes for a patient with a higher accuracy by combining the results of different machine learning techniques. The algorithms like K nearest neighbour, Logistic Regression, Random forest, Support vector machine and Decision tree are used. The accuracy of the model using each of the algorithms is calculated. Then the one with a good accuracy is taken as the model for predicting the diabetes.
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Zheng, Yineng, Xingming Guo, Yingying Wang, Jian Qin, and Fajin Lv. "A multi-scale and multi-domain heart sound feature-based machine learning model for ACC/AHA heart failure stage classification." Physiological Measurement, May 5, 2022. http://dx.doi.org/10.1088/1361-6579/ac6d40.
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Abstract Objective: Heart sounds can reflect detrimental changes in cardiac mechanical activity that are common pathological characteristics of chronic heart failure (CHF). The ACC/AHA heart failure (HF) stage classification is essential for clinical decision-making and the management of CHF. Herein, a machine learning model that makes use of multi-scale and multi-domain heart sound features was proposed to provide an objective aid for ACC/AHA HF stage classification. Approach: A dataset containing phonocardiogram (PCG) signals from 275 subjects was obtained from two medical institutions and used in this study. Complementary ensemble empirical mode decomposition and tunable-Q wavelet transform were used to construct self-adaptive sub-sequences and multi-level sub-band signals for PCG signals. Time-domain, frequency-domain and nonlinear feature extraction were then applied to the original PCG signal, heart sound sub-sequences and sub-band signals to construct multi-scale and multi-domain heart sound features. The features selected via the least absolute shrinkage and selection operator were fed into a machine learning classifier for ACC/AHA HF stage classification. Finally, mainstream machine learning classifiers, including least-squares support vector machine (LS-SVM), deep belief network (DBN) and random forest (RF), were compared to determine the optimal model. Main results: The results showed that the LS-SVM, which utilized a combination of multi-scale and multi-domain features, achieved better classification performance than the DBN and RF using multi-scale or multi-domain features alone or together, with average sensitivity, specificity, and accuracy of 0.821, 0.955 and 0.820 on the testing set, respectively. Significance: PCG signal analysis provides efficient measurement information regarding CHF severity and is a promising noninvasive method for ACC/AHA HF stage classification.
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McBeath, K. C. C., C. E. Angermann, and M. R. Cowie. "Digital Technologies to Support Better Outcome and Experience of Care in Patients with Heart Failure." Current Heart Failure Reports, April 29, 2022. http://dx.doi.org/10.1007/s11897-022-00548-z.
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Abstract Purpose of Review In this article, we review a range of digital technologies for possible application in heart failure patients, with a focus on lessons learned. We also discuss a future model of heart failure management, as digital technologies continue to become part of standard care. Recent Findings Digital technologies are increasingly used by healthcare professionals and those living with heart failure to support more personalised and timely shared decision-making, earlier identification of problems, and an improved experience of care. The COVID-19 pandemic has accelerated the acceptability and implementation of a range of digital technologies, including remote monitoring and health tracking, mobile health (wearable technology and smartphone-based applications), and the use of machine learning to augment data interpretation and decision-making. Much has been learned over recent decades on the challenges and opportunities of technology development, including how best to evaluate the impact of digital health interventions on health and healthcare, the human factors involved in implementation and how best to integrate dataflows into the clinical pathway. Summary Supporting patients with heart failure as well as healthcare professionals (both with a broad range of health and digital literacy skills) is crucial to success. Access to digital technologies and the internet remains a challenge for some patients. The aim should be to identify the right technology for the right patient at the right time, in a process of co-design and co-implementation with patients.
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Navin, K. S., H. Khanna Nehemiah, Y. Nancy Jane, and H. Veena Saroji. "A classification framework using filter–wrapper based feature selection approach for the diagnosis of congenital heart failure." Journal of Intelligent & Fuzzy Systems, January 19, 2023, 1–36. http://dx.doi.org/10.3233/jifs-221348.
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Premature mortality from cardiovascular disease can be reduced with early detection of heart failure by analysing the patients’ risk factors and assuring accurate diagnosis. This work proposes a clinical decision support system for the diagnosis of congenital heart failure by utilizing a data pre-processing approach for dealing missing values and a filter-wrapper based method for selecting the most relevant features. Missing values are imputed using a missForest method in four out of eight heart disease datasets collected from the Machine Learning Repository maintained by University of California, Irvine. The Fast Correlation Based Filter is used as the filter approach, while the union of the Atom Search Optimization Algorithm and the Henry Gas Solubility Optimization represent the wrapper-based algorithms, with the fitness function as the combination of accuracy, G-mean, and Matthew’s correlation coefficient measured by the Support Vector Machine. A total of four boosted classifiers namely, XGBoost, AdaBoost, CatBoost, and LightGBM are trained using the selected features. The proposed work achieves an accuracy of 89%, 84%, 83%, 80% for Heart Failure Clinical Records, 81%, 80%, 83%, 82% for Single Proton Emission Computed Tomography, 90%, 82%, 93%, 80% for Single Proton Emission Computed Tomography F, 80%, 80%, 81%, 80% for Statlog Heart Disease, 80%, 85%, 83%, 86% for Cleveland Heart Disease, 82%, 85%, 85%, 82% for Hungarian Heart Disease, 80%, 81%, 79%, 82% for VA Long Beach, 97%, 89%, 98%, 97%, for Switzerland Heart Disease for four classifiers respectively. The suggested technique outperformed the other classifiers when evaluated against Random Forest, Classification and Regression Trees, Support Vector Machine, and K-Nearest Neighbor.
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Luo, Cida, Yi Zhu, Zhou Zhu, Ranxi Li, Guoqin Chen, and Zhang Wang. "A machine learning-based risk stratification tool for in-hospital mortality of intensive care unit patients with heart failure." Journal of Translational Medicine 20, no. 1 (March 18, 2022). http://dx.doi.org/10.1186/s12967-022-03340-8.
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Abstract Background Predicting hospital mortality risk is essential for the care of heart failure patients, especially for those in intensive care units. Methods Using a novel machine learning algorithm, we constructed a risk stratification tool that correlated patients’ clinical features and in-hospital mortality. We used the extreme gradient boosting algorithm to generate a model predicting the mortality risk of heart failure patients in the intensive care unit in the derivation dataset of 5676 patients from the Medical Information Mart for Intensive Care III database. The logistic regression model and a common risk score for mortality were used for comparison. The eICU Collaborative Research Database dataset was used for external validation. Results The performance of the machine learning model was superior to that of conventional risk predictive methods, with the area under curve 0.831 (95% CI 0.820–0.843) and acceptable calibration. In external validation, the model had an area under the curve of 0.809 (95% CI 0.805–0.814). Risk stratification through the model was specific when the hospital mortality was very low, low, moderate, high, and very high (2.0%, 10.2%, 11.5%, 21.2% and 56.2%, respectively). The decision curve analysis verified that the machine learning model is the best clinically valuable in predicting mortality risk. Conclusion Using readily available clinical data in the intensive care unit, we built a machine learning-based mortality risk tool with prediction accuracy superior to that of linear regression model and common risk scores. The risk tool may support clinicians in assessing individual patients and making individualized treatment.
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Lin, Gen-Min. "Abstract P181: Prediction Of Left Ventricular Diastolic Dysfunction Using Electrocardiographic Machine Learning In Asian Young Male Adults." Circulation 143, Suppl_1 (May 25, 2021). http://dx.doi.org/10.1161/circ.143.suppl_1.p181.
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Left ventricular diastolic dysfunction (LVDD) occurs at initial stage of heart failure. Electrocardiographic (ECG) criteria and machine learning for ECG features with or wihtout biological features have been applied successfully to predict LVDD in middle- and old-aged individuals. The purpose of this study is to clarify the performance of machine learning in young adults. In a large sample of 2,206 military males in Hualien, Taiwan, aged 17-43 years, the prevalence of LVDD is 4.26%. Five machine learning classifiers including random forest (RF), support vector machine (SVM), gradient boosting decision tree (GBDT), multi-layer perceptron (MLP) and logistic regression (LR) for the input of 26 ECG features with or without other 6 biological features (age, anthropometrics, and blood pressures) to link the output of LVDD are compared with the corrected QT interval (QTc) calculated by the Bazett’s formula, a traditional ECG criterion for LVDD. The definition of LVDD is based on either one of the echocardiographic criteria: (1) the E/A ratio of the mitral inflow <0.8; (2) the lateral mitral annulus velocity, e’ <10 cm/s; and (3) the E/e’ ratio >14. The area under the receiver operating characteristic (ROC) curve in machine learning of the RF, SVM, GBDT, MLP and LR for ECG only are 84.1%, 78.7%, 77.9%, 77.6% and 75.4%, which are all superior to 64.6% in the QTc interval. If the specificity is fixed around 70-80%, the sensitivity of these mahine learning classifiers for ECG only are 81.0%, 76.2%, 71.4%, 71.4% and 71.4%, which are all higher than 47.6% in the QTc interval. This study suggests that using machine learning for ECG features only to predict LVDD in Asian young adults is reliable and thereby it is possible for people to take an early preventive action on heart failure.
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"Diabetes Mellitus Prediction using Ensemble Machine Learning Techniques." Regular 9, no. 2 (July 30, 2020): 312–16. http://dx.doi.org/10.35940/ijrte.b3480.079220.
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The healthcare industry is inflicted with the plethora of patient data which is being supplemented each day manifold. Researchers have been continually using this data to help the healthcare industry improve upon the way major diseases could be handled. They are even working upon the way the patients could be informed timely of the symptoms that could avoid the major hazards related to them. Diabetes is one such disease that is growing at an alarming rate today. In fact, it can inflict numerous severe damages; blurred vision, myopia, burning extremities, kidney and heart failure. It occurs when sugar levels reach a certain threshold, or the human body cannot contain enough insulin to regulate the threshold. Therefore, patients affected by Diabetes must be informed so that proper treatments can be taken to control Diabetes. For this reason, early prediction and classification of Diabetes are significant. This work makes use of Machine Learning algorithms to improve the accuracy of prediction of the Diabetes. A dataset obtained as an output of K-Mean Clustering Algorithm was fed to an ensemble model with principal component analysis and K-means clustering. Our ensemble method produced only eight incorrectly classified instances, which was lowest compared to other methods. The experiments also showed that ensemble classifier models performed better than the base classifiers alone. Its result was compared with the same Dataset being applied on specific methods like random forest, Support Vector Machine, Decision Tree, Multilayer perceptron, and Naïve Bayes classification methods. All methods were run using 10k fold cross-validation.
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Liu, Wen Tao, Xiao Qi Liu, Ting Ting Jiang, Meng Ying Wang, Yang Huang, Yu Lin Huang, Feng Yong Jin, et al. "Using a machine learning model to predict the development of acute kidney injury in patients with heart failure." Frontiers in Cardiovascular Medicine 9 (September 7, 2022). http://dx.doi.org/10.3389/fcvm.2022.911987.
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BackgroundHeart failure (HF) is a life-threatening complication of cardiovascular disease. HF patients are more likely to progress to acute kidney injury (AKI) with a poor prognosis. However, it is difficult for doctors to distinguish which patients will develop AKI accurately. This study aimed to construct a machine learning (ML) model to predict AKI occurrence in HF patients.Materials and methodsThe data of HF patients from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database was retrospectively analyzed. A ML model was established to predict AKI development using decision tree, random forest (RF), support vector machine (SVM), K-nearest neighbor (KNN), and logistic regression (LR) algorithms. Thirty-nine demographic, clinical, and treatment features were used for model establishment. Accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUROC) were used to evaluate the performance of the ML algorithms.ResultsA total of 2,678 HF patients were engaged in this study, of whom 919 developed AKI. Among 5 ML algorithms, the RF algorithm exhibited the highest performance with the AUROC of 0.96. In addition, the Gini index showed that the sequential organ function assessment (SOFA) score, partial pressure of oxygen (PaO2), and estimated glomerular filtration rate (eGFR) were highly relevant to AKI development. Finally, to facilitate clinical application, a simple model was constructed using the 10 features screened by the Gini index. The RF algorithm also exhibited the highest performance with the AUROC of 0.95.ConclusionUsing the ML model could accurately predict the development of AKI in HF patients.
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Gutman, R., U. Shalit, O. Caspi, and D. Aronson. "What drives success in models predicting heart failure outcome?" European Heart Journal 41, Supplement_2 (November 1, 2020). http://dx.doi.org/10.1093/ehjci/ehaa946.3556.
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Abstract Introduction The development of acute heart failure (AHF) is a critical decision-point in the natural history of heart failure and carries a dismal prognosis. The lack of appropriate risk-stratification tools for AHF patients limits physician ability to precisely tailor patient-specific therapy regimen at this important juncture. Machine learning (ML) based strategies may enhance risk stratification by incorporating analysis of high-dimensional patient data with multiple covariates and novel prediction methodologies. In this study, we aimed at evaluating the drivers for success in prediction models and establishing an institute-tailored ML-based prediction model for real-time decision support. Methods We used a cohort of all AHF patients admitted during a 12 years period including 10,868 patients. A total of 372 covariates were collected from admission to the end of the hospitalization (demographics, lab-tests, medical therapies, echocardiographic and administrative data). Data preprocessing included features cleaning, train-test split, imputation and normalization. We assessed model performance across two axes (1) type of prediction method and (2) type and number of covariates. The primary outcome was one-year survival from hospital discharge. For the model-type axis we experimented with seven different methods: Logistic Regression (LR), Random Forest (RF), Cox model (Cox), XGBoost, a deep neural-network (NeuralNet) and an ensembled model. Results Data pre-processing methodology combined with multiple-covariates achieved an out-ofsample AUROC prediction accuracy of more than 80% with almost all prediction models: L1/L2-LR (80.4%/80.3%); Cox (80.1%); XGBoost (80.7%); NeuralNet (80.5%). The number of covariates was a significant modifier of prediction success (p<0.001), the use of multiple-covariates (372) performed better (AUROC 80.4% for L1-LR) compared with using only a set of known clinical covariates (AUROC 77.8%). Conclusions The choice of the predictive modeling method is secondary to the multiplicity and type of covariates for predicting AHF prognosis. The application of a structured data pre-processing combined with the use of multiple-covariates results in an accurate, institute-tailored, risk prediction in AHF. Funding Acknowledgement Type of funding source: Foundation. Main funding source(s): Yad Hanadiv
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"Prediction of Patient Readmission via Machine Learning Algorithms." International Journal of Recent Technology and Engineering 8, no. 6 (March 30, 2020): 3226–32. http://dx.doi.org/10.35940/ijrte.f7770.038620.
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Predicting the probability of hospital readmission is one of the most vital issues and is considered to be an important research area in the healthcare sector. For curing any of the diseases that might arise, there shall be some essential resources such as medical staff, expertise, beds and rooms. This secures getting excellent medical service. For example, heart failure (HF) or diabetes is a syndrome that could reduce the living quality of patients and has a serious influence on systems of healthcare. The previously mentioned diseases can result in high rate of readmission and hence high rate of costs as well. In this case, algorithms of machine learning are utilized to curb readmissions levels and improve the life quality of patients. Unluckily, a comparatively few numbers of researches in the literature endeavored to address this issue while a large proportion of researches were interested in predicting the probability of detecting diseases. Despite there is a plainly visible shortage on this topic, this paper seeks to spot most of the studies related to predict the probability of hospital readmission by the usage of machine learning techniques such as such as Logistic Regression (LR), Support Vector Machine (SVM), Artificial Neural Networks (ANNs), Linear Discriminant Analysis (LDA), Bayes algorithm, Random Forest (RF), Decision Trees (DTs), AdaBoost and Gradient Boosting (GB). Specifically, we explore the different techniques used in a medical area under the machine learning research field. In addition, we define four features that are used as criteria for an effective comparison among the employed techniques. These features include goal, data size, method, and performance. Furthermore, some recommendations are drawn from the comparison which is related to the selection of the best techniques in the medical field. Based on the outcomes of this research, it was found out that (bagging and DT) is the best technique to predict diabetes, whereas SVM is the best technique when it comes to prediction the breast cancer, and hospital readmission.
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Radhachandran, Ashwath, Anurag Garikipati, Nicole S. Zelin, Emily Pellegrini, Sina Ghandian, Jacob Calvert, Jana Hoffman, Qingqing Mao, and Ritankar Das. "Prediction of short-term mortality in acute heart failure patients using minimal electronic health record data." BioData Mining 14, no. 1 (March 31, 2021). http://dx.doi.org/10.1186/s13040-021-00255-w.
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Abstract Background Acute heart failure (AHF) is associated with significant morbidity and mortality. Effective patient risk stratification is essential to guiding hospitalization decisions and the clinical management of AHF. Clinical decision support systems can be used to improve predictions of mortality made in emergency care settings for the purpose of AHF risk stratification. In this study, several models for the prediction of seven-day mortality among AHF patients were developed by applying machine learning techniques to retrospective patient data from 236,275 total emergency department (ED) encounters, 1881 of which were considered positive for AHF and were used for model training and testing. The models used varying subsets of age, sex, vital signs, and laboratory values. Model performance was compared to the Emergency Heart Failure Mortality Risk Grade (EHMRG) model, a commonly used system for prediction of seven-day mortality in the ED with similar (or, in some cases, more extensive) inputs. Model performance was assessed in terms of area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity. Results When trained and tested on a large academic dataset, the best-performing model and EHMRG demonstrated test set AUROCs of 0.84 and 0.78, respectively, for prediction of seven-day mortality. Given only measurements of respiratory rate, temperature, mean arterial pressure, and FiO2, one model produced a test set AUROC of 0.83. Neither a logistic regression comparator nor a simple decision tree outperformed EHMRG. Conclusions A model using only the measurements of four clinical variables outperforms EHMRG in the prediction of seven-day mortality in AHF. With these inputs, the model could not be replaced by logistic regression or reduced to a simple decision tree without significant performance loss. In ED settings, this minimal-input risk stratification tool may assist clinicians in making critical decisions about patient disposition by providing early and accurate insights into individual patient’s risk profiles.
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van der Galiën, Onno P., René C. Hoekstra, Muhammed T. Gürgöze, Olivier C. Manintveld, Mark R. van den Bunt, Cor J. Veenman, and Eric Boersma. "Prediction of long-term hospitalisation and all-cause mortality in patients with chronic heart failure on Dutch claims data: a machine learning approach." BMC Medical Informatics and Decision Making 21, no. 1 (November 1, 2021). http://dx.doi.org/10.1186/s12911-021-01657-w.
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Abstract Background Accurately predicting which patients with chronic heart failure (CHF) are particularly vulnerable for adverse outcomes is of crucial importance to support clinical decision making. The goal of the current study was to examine the predictive value on long term heart failure (HF) hospitalisation and all-cause mortality in CHF patients, by exploring and exploiting machine learning (ML) and traditional statistical techniques on a Dutch health insurance claims database. Methods Our study population consisted of 25,776 patients with a CHF diagnosis code between 2012 and 2014 and one year and three years follow-up HF hospitalisation (1446 and 3220 patients respectively) and all-cause mortality (2434 and 7882 patients respectively) were measured from 2015 to 2018. The area under the receiver operating characteristic (ROC) curve (AUC) was calculated after modelling the data using Logistic Regression, Random Forest, Elastic Net regression and Neural Networks. Results AUC rates ranged from 0.710 to 0.732 for 1-year HF hospitalisation, 0.705–0.733 for 3-years HF hospitalisation, 0.765–0.787 for 1-year mortality and 0.764–0.791 for 3-years mortality. Elastic Net performed best for all endpoints. Differences between techniques were small and only statistically significant between Elastic Net and Logistic Regression compared with Random Forest for 3-years HF hospitalisation. Conclusion In this study based on a health insurance claims database we found clear predictive value for predicting long-term HF hospitalisation and mortality of CHF patients by using ML techniques compared to traditional statistics.
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Zhao, Claire Y. "Abstract 13386: Individualized Risk Score Interpretation to Aid Clinical Decisions and Transitions of Care for Acute Heart Failure Patients." Circulation 142, Suppl_3 (November 17, 2020). http://dx.doi.org/10.1161/circ.142.suppl_3.13386.
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Acute heart failure (AHF) is a complex disease with heterogeneous manifestations and adverse outcomes. The interpretation of machine-learning risk scores is vital to support clinical decisions. Individualized Feature Importance (IFI) was designed to attribute changes in risk scores to clinical features and help contrast decision trajectory for a patient against those of patient subgroups that received distinct clinical decisions. Score Confidence Interval (SCI) was developed to quantify certainty in the prediction, which further encourages clinicians’ interpretation. Study was based on retrospective data from 25 hospitals in the US of 20,640 adult patients, with 87% discharged home (Class 0) and 13% transferred to the ICU or died in hospital (Class 1). IFI is based on Shapley Value, based on which SCI was designed to capture the variation in score if input features are missing. These methods were applied to previously developed risk score for AHF patients in the wards; however, they can be applied to any risk score. The SCI is wide at the beginning of the stay and narrows down towards the end as more clinical measurements become available, indicating the risk score is relatively certain at the end (Fig. 1a). IFI values (Fig. 1b) show how selected features drive changes in the risk score. To aid decision-making at the latest time, top missing features are prompted (Fig. 1c). Decision trajectories show the way top features drive the risk score (Fig. 1d), that this patient is at higher risk to discharge (Fig. 1e) and is more similar to ICU-transfers (Fig. 1f). Fig. 1g shows SCI improves risk score performance by abstaining uncertain cases from decision-making. IFI apportions risk score to clinical measurements. SCI reduces false alarm rates. By providing clinical context, they have the potential to enhance incorporation of risk scores in the clinical workflow to aid medical decisions by identifying patients at risk for deterioration and determining appropriate levels of care.