Littérature scientifique sur le sujet « Heart Failure, Machine Learning, Decision Support, Telemonitoring »
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Articles de revues sur le sujet "Heart Failure, Machine Learning, Decision Support, Telemonitoring"
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Nagavelli, Umarani, Debabrata Samanta et Partha Chakraborty. « Machine Learning Technology-Based Heart Disease Detection Models ». Journal of Healthcare Engineering 2022 (27 février 2022) : 1–9. http://dx.doi.org/10.1155/2022/7351061.
Texte intégralRésumé :
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, et Afidalina Tumian. « Variables Influencing Machine Learning-Based Cardiac Decision Support System : A Systematic Literature Review ». Applied Mechanics and Materials 892 (juin 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 et Son V.T Dao. « Predicting heart failure using a wrapper-based feature selection ». Indonesian Journal of Electrical Engineering and Computer Science 21, no 3 (10 mars 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, et Rakesh M D. « Prediction of Cardiac Arrhythmia using Machine Learning ». International Journal for Research in Applied Science and Engineering Technology 10, no 9 (30 septembre 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 et 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 (11 octobre 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 et 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 (9 mars 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 et Kyung Sup Kwak. « Detecting Congestive Heart Failure by Extracting Multimodal Features and Employing Machine Learning Techniques ». BioMed Research International 2020 (18 février 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, et Abdulwahab Alazeb. « A Model for Predicting Cervical Cancer Using Machine Learning Algorithms ». Sensors 22, no 11 (29 mai 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 et Ruinan Sun. « A Hybrid Intelligent System Framework for the Prediction of Heart Disease Using Machine Learning Algorithms ». Mobile Information Systems 2018 (2 décembre 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 et D. McEneaneny. « What can machines learn about heart failure ? A systematic literature review ». International Journal of Data Science and Analytics 13, no 3 (30 décembre 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.
Thèses sur le sujet "Heart Failure, Machine Learning, Decision Support, Telemonitoring"
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GUIDI, GABRIELE. « System for aiding clinical management of congestive heart failure to improve patient assistance at home ». Doctoral thesis, 2017. http://hdl.handle.net/2158/1080160.
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Congestive Heart Failure (CHF) is a serious cardiac condition that brings high risks of urgent hospitalization and death. Remote monitoring systems are well-suited to manage patients suffering from CHF and can reduce deaths and re-hospitalizations, as shown by the literature, including multiple systematic reviews. The monitoring system proposed in this thesis aims to help CHF stakeholders to make appropriate decisions in managing the disease and preventing cardiac events, such as decompensation, which can lead to hospitalization or death. The whole thesis work is composed of a part of research about the analysis of the typical CHF clinical pathways and its monitoring procedures, and a part of research and innovative development that aims to create software and models (machine learning) useful to the various stakeholders of care processes. In order to include our system in a feasible clinical pathway we proposed a CHF monitoring scenario stratified into three layers: 1-Hospital scheduled visits performed by cardiologist, 2-home monitoring visits performed by nurses, and 3-home monitoring measurements performed by the patient using specialized equipment. Appropriate desktop and mobile software applications were developed in this thesis work to enable such multilayer CHF monitoring. For the first two layers, we designed and implemented a Decision Support System (DSS) using machine learning (Random Forest algorithm) to predict the number of exacerbations per year and to assess the CHF severity based on a variety of clinical data. This represents the research core of this thesis. Performances of the trained machine learning techniques are established using k-folds Cross Validation method and, if compared with literature, results are good (81.3% multiclass-accuracy in severity assessment and 71.9% in prediction of exacerbations). For the third layer, we contacted the University of Houston who has developed some custom-designed sensors (the Blue Scale system) for electrocardiogram (EKG), pulse transit times, bio-impedance and weight allowed frequent collection of CHF-related data in the comfort of the patient’s home. It was then performed a study on possible commercial cloud “analytics as a service” solutions to process biometric signals and to build a predictor system for the early detection of Heart Failure.
Chapitres de livres sur le sujet "Heart Failure, Machine Learning, Decision Support, Telemonitoring"
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Mansoor, Nijatullah, Ramesh Chandra Poonia et Debabrata Samanta. « Predictive Analysis of Diabetes Using Machine Learning Algorithms ». Dans Advances in Healthcare Information Systems and Administration, 338–52. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-4580-8.ch018.
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Diabetes is a very harmful disease that causes high blood sugar levels and occurs when the blood glucose level is high. Diabetes causes numerous diseases in humans: congestive heart failure, stroke, kidney and eye problems, dental issues, nerve damage, and foot problems. With the recent development in the machine learning concept, it is easy to analyze and predict whether a person is diabetic or not. This research mainly focuses on using several prediction algorithms of machine learning. The algorithms used in this research are k-nearest neighbor, logistic regression, SVM (support vector machine), Gaussian naive Bayes, decision tree, multilayer perceptron, random forest, XGBoost, and AdaBoost. Among these algorithms, the XGBoost performed better than the other algorithms achieving an accuracy of 90%, and the f1 score and Jaccard score were 91% and 86%, respectively. The primary goal of this research is to apply numerous machine learning algorithms to diabetic datasets, analyze their results, and select the best one that performs well.