Journal articles on the topic 'Random Forest, Questionnaire, Cardiovascular diseases'
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Butkevičiūtė, Eglė, Liepa Bikulčienė, and Aušra Žvironienė. "Physiological State Evaluation in Working Environment Using Expert System and Random Forest Machine Learning Algorithm." Healthcare 11, no. 2 (January 11, 2023): 220. http://dx.doi.org/10.3390/healthcare11020220.
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Healthy lifestyle is one of the most important factors in the prevention of premature deaths, chronic diseases, productivity loss, obesity, and other economic and social aspects. The workplace plays an important role in promoting the physical activity and wellbeing of employees. Previous studies are mostly focused on individual interviews, various questionnaires that are a conceptual information about individual health state and might change according to question formulation, specialist competence, and other aspects. In this paper the work ability was mostly related to the employee’s physiological state, which consists of three separate systems: cardiovascular, muscular, and neural. Each state consists of several exercises or tests that need to be performed one after another. The proposed data transformation uses fuzzy logic and different membership functions with three or five thresholds, according to the analyzed physiological feature. The transformed datasets are then classified into three stages that correspond to good, moderate, and poor health condition using machine learning techniques. A three-part Random Forest method was applied, where each part corresponds to a separate system. The obtained testing accuracies were 93%, 87%, and 73% for cardiovascular, muscular, and neural human body systems, respectively. The results indicate that the proposed work ability evaluation process may become a good tool for the prevention of possible accidents at work, chronic fatigue, or other health problems.
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Basnet, Til Bahadur, Srijana G. C., Rajesh Basnet, and Bidusha Neupane. "Dietary nutrients of relative importance associated with coronary artery disease: Public health implication from random forest analysis." PLOS ONE 15, no. 12 (December 10, 2020): e0243063. http://dx.doi.org/10.1371/journal.pone.0243063.
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Dietary nutrients have significant effects on the risk of cardiovascular diseases. However, the results were not uniform across different countries. The study aims to determine the relative importance of dietary nutrients associated with coronary artery disease (CAD) among the Nepalese population. A hospital-based matched case-control study was carried out at Shahid Gangalal National Heart Center in Nepal. In the present study, patients with more than seventy percent stenosis in any main coronary artery branch in angiography were defined as cases, while those presenting normal coronary angiography or negative for stressed exercise test were considered controls. Dietary intakes of 612 respondents over the past 12 months were evaluated using a semi-quantitative customized food frequency questionnaire. In conditional regression model, the daily average dietary intake of β-carotene (OR: 0.54; 95%CI: 0.34, 0.87), and vitamin C (OR: 0.96; 95%CI: 0.93, 0.99) were inversely, whereas dietary carbohydrate (OR: 1.16; 95%CI: 1.1, 1.24), total fat/oil (OR: 1.47; 95%CI: 1.27, 1.69), saturated fatty acid (SFA) (OR: 1.2; 95%CI: 1.11, 1.3), cholesterol (OR: 1.01; 95%CI: 1.001, 1.014), and iron intakes (OR: 1.11; 95%CI: 1.03, 1.19) were positively linked with CAD. Moreover, in random forest analysis, the daily average dietary intakes of SFA, vitamin A, total fat/oil, β-carotene, and cholesterol were among the top five nutrients (out of 12 nutrients variables) of relative importance associated with CAD. The nutrients of relative importance imply a reasonable preventive measure in public health nutrients specific intervention to prevent CAD in a resource-poor country like Nepal. The findings are at best suggestive of a possible relationship between these nutrients and the development of CAD, but prospective cohort studies and randomized control trials will need to be performed in the Nepalese population.
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R., Vasanthi,, and Tamilselvi, J. "Heart Disease Prediction Using Random Forest Algorithm." CARDIOMETRY, no. 24 (November 30, 2022): 982–88. http://dx.doi.org/10.18137/cardiometry.2022.24.982988.
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Heart disease is one of the complex diseases and globally many of us suffer from this disease. On time and efficient identification of cardiovascular disease plays a key role in healthcare, particularly within the field of cardiology. An efficient and accurate system to diagnose cardiovascular disease and the system is predicated on machine learning techniques. The system is developed by classification algorithms using Random Forest, Naïve Bayes and Support Vector Machine while standard features selection techniques are used like univerate, feature importance , and correlation matrix for removing irrelevant and redundant features. The features selection are used for feature to extend the classification accuracy and reduce the execution time of the arrangement. The way that aims at finding significant features by applying machine learning techniques leading to improving the accuracy within the prediction of disorder. The heart disease prediction that Random Forest achieved good accuracy as compared to other algorithms.
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Srınıvasa Rao, B. "A New Ensenble Learning based Optimal Prediction Model for Cardiovascular Diseases." E3S Web of Conferences 309 (2021): 01007. http://dx.doi.org/10.1051/e3sconf/202130901007.
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The present paperreports an optimal machine learning model for an effective prediction of cardiovascular diseases that uses the ensemble learning technique. The present research work gives an insight about the coherent way of combining Naive Bayes and Random Forest algorithm using ensemble technique. It also discusses how the present model is different from other traditional approaches. The present experimental results manifest that the present optimal machine learning model is more efficient than the other models.
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Worachartcheewan, Apilak, Watshara Shoombuatong, Phannee Pidetcha, Wuttichai Nopnithipat, Virapong Prachayasittikul, and Chanin Nantasenamat. "Predicting Metabolic Syndrome Using the Random Forest Method." Scientific World Journal 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/581501.
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Aims. This study proposes a computational method for determining the prevalence of metabolic syndrome (MS) and to predict its occurrence using the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) criteria. The Random Forest (RF) method is also applied to identify significant health parameters.Materials and Methods. We used data from 5,646 adults aged between 18–78 years residing in Bangkok who had received an annual health check-up in 2008. MS was identified using the NCEP ATP III criteria. The RF method was applied to predict the occurrence of MS and to identify important health parameters surrounding this disorder.Results. The overall prevalence of MS was 23.70% (34.32% for males and 17.74% for females). RF accuracy for predicting MS in an adult Thai population was 98.11%. Further, based on RF, triglyceride levels were the most important health parameter associated with MS.Conclusion. RF was shown to predict MS in an adult Thai population with an accuracy >98% and triglyceride levels were identified as the most informative variable associated with MS. Therefore, using RF to predict MS may be potentially beneficial in identifying MS status for preventing the development of diabetes mellitus and cardiovascular diseases.
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Bhatt, Chintan M., Parth Patel, Tarang Ghetia, and Pier Luigi Mazzeo. "Effective Heart Disease Prediction Using Machine Learning Techniques." Algorithms 16, no. 2 (February 6, 2023): 88. http://dx.doi.org/10.3390/a16020088.
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The diagnosis and prognosis of cardiovascular disease are crucial medical tasks to ensure correct classification, which helps cardiologists provide proper treatment to the patient. Machine learning applications in the medical niche have increased as they can recognize patterns from data. Using machine learning to classify cardiovascular disease occurrence can help diagnosticians reduce misdiagnosis. This research develops a model that can correctly predict cardiovascular diseases to reduce the fatality caused by cardiovascular diseases. This paper proposes a method of k-modes clustering with Huang starting that can improve classification accuracy. The underlying model applies DBSCAN to remove outliers. Models such as random forest (RF), decision tree classifier (DT), multilayer perceptron (MP), and XGBoost (XGB) are used. GridSearchCV was used to hypertune the parameters of the applied model to optimize the result. The proposed model is applied to a real-world dataset of 70,000 instances from Kaggle. Models were trained on data that were split in 80:20 and achieved accuracy as follows: decision tree: 86.37% (with cross-validation) and 86.53% (without cross-validation), XGBoost: 86.87% (with cross-validation) and 87.02% (without cross-validation), random forest: 87.05% (with cross-validation) and 86.92% (without cross-validation), multilayer perceptron: 87.28% (with cross-validation) and 86.94% (without cross-validation). The proposed models have AUC (area under the curve) values: decision tree: 0.94, XGBoost: 0.95, random forest: 0.95, multilayer perceptron: 0.95. The conclusion drawn from this underlying research is that multilayer perceptron with cross-validation has outperformed all other algorithms in terms of accuracy. It achieved the highest accuracy of 87.28%.
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Yekkala, Indu, and Sunanda Dixit. "Prediction of Heart Disease Using Random Forest and Rough Set Based Feature Selection." International Journal of Big Data and Analytics in Healthcare 3, no. 1 (January 2018): 1–12. http://dx.doi.org/10.4018/ijbdah.2018010101.
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Data is generated by the medical industry. Often this data is of very complex nature—electronic records, handwritten scripts, etc.—since it is generated from multiple sources. Due to the Complexity and sheer volume of this data necessitates techniques that can extract insight from this data in a quick and efficient way. These insights not only diagnose the diseases but also predict and can prevent disease. One such use of these techniques is cardiovascular diseases. Heart disease or coronary artery disease (CAD) is one of the major causes of death all over the world. Comprehensive research using single data mining techniques have not resulted in an acceptable accuracy. Further research is being carried out on the effectiveness of hybridizing more than one technique for increasing accuracy in the diagnosis of heart disease. In this article, the authors worked on heart stalog dataset collected from the UCI repository, used the Random Forest algorithm and Feature Selection using rough sets to accurately predict the occurrence of heart disease
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Sun, Weicheng, Ping Zhang, Zilin Wang, and Dongxu Li. "Prediction of Cardiovascular Diseases based on Machine Learning." ASP Transactions on Internet of Things 1, no. 1 (May 29, 2021): 30–35. http://dx.doi.org/10.52810/tiot.2021.100035.
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With the rapid development of artificial intelligence, it is very important to find the pattern of the data from the observed data and the functional dependency relationship between the data. By finding the existing functional dependencies, we can classify and predict them. At present, cardiovascular disease has become a major disease harmful to human health. As a disease with high mortality, the prediction problem of cardiovascular disease is becoming more and more urgent. However, some computer methods are mainly used for disease detection rather than prediction. If the computer method can be used to predict cardiovascular disease in advance and treat it as early as possible, then the consequences of the disease can be reduced to a certain extent. Diseases can be predicted by mechanical methods. Support vector machine (SVM) has strict mathematical theory support, and can deal with nonlinear classification after using kernel techniques. Therefore, support vector machine can be used to predict cardiovascular disease. On the other hand, we also use logical regression and random forest to predict cardiovascular disease. This paper mainly uses the method of machine learning to predict whether the population is sick or not. First of all, we preprocess the obtained data to improve the quality of the data, and then use svm and logical regression to predict, so as to provide reference for the prevention and treatment of cardiovascular diseases.
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Osemeobo, Gbadebo Jonathan. "Can Food Crop Medicine Reduce Pressure on Forest Harvest in Nigeria?" Dutse Journal of Pure and Applied Sciences 7, no. 3a (January 3, 2022): 23–31. http://dx.doi.org/10.4314/dujopas.v7i3a.3.
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Awareness created in the social media through smart phones had popularized the practice of using food crop parts such as leaves, fruits, roots and seeds to treat common illness which had hitherto been treated with herbs from the wild. This study investigated whether the use of food crops for medicine can reduce pressures of harvesting medicinal plants from the forest. A combination of three research methods: structured questionnaire survey; informal discussions with stakeholders in food crop medicines and observations on collection and preparation of food crop medicines were used to collect data. Simple random sampling method was used to select 62 respondents for questionnaire survey and discussion groups in Ota Ogun State, Nigeria. Data were presented in tables and percentages. A Chi-square analysis was used to test the research hypothesis. Results derived from data analyses indicated that food crop medicine (FCM) was: (i) widely used; (ii) fully accepted; (iii) gradually reducing pressures in natural forests; and (iv) used to compliment indigenous traditional medicine. A conclusion was reached that FCM had come to stay as a major primary health delivery. Moreover, FCM has found a place in herbal treatments of diseases.
Keywords: Food crop medicine, Traditional medicine, Traditional plants, Natural forests, Herbs.
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Navarrete, Jean Paul, Jose Pinto, Rosa Liliana Figueroa, Maria Elena Lagos, Qing Zeng, and Carla Taramasco. "Supervised Learning Algorithm for Predicting Mortality Risk in Older Adults Using Cardiovascular Health Study Dataset." Applied Sciences 12, no. 22 (November 14, 2022): 11536. http://dx.doi.org/10.3390/app122211536.
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Multiple chronic conditions are an important factor influencing mortality in older adults. At the same time, cardiovascular events in older adult patients are one of the leading causes of mortality worldwide. This study aimed to design a machine learning model capable of predicting mortality risk in older adult patients with cardiovascular pathologies and multiple chronic diseases using the Cardiovascular Health Study database. The methodology for algorithm design included (i) database analysis, (ii) variable selection, (iii) feature matrix creation and data preprocessing, (iv) model training, and (v) performance analysis. The analysis and variable selection were performed through previous knowledge, correlation, and histograms to visualize the data distribution. The machine learning models selected were random forest, support vector machine, and logistic regression. The models were trained using two sets of variables. First, eight years of the data were summarized as the mode of all years per patient for each variable (123 variables). The second set of variables was obtained from the mode every three years (369 variables). The results show that the random forest trained with the second set of variables has the best performance (89% accuracy), which is better than other reported results in the literature.
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Askari, GholamReza, Mohammad Hossein Rouhani, and Mohammad Sattari. "Prediction of Length of Hospital Stay of COVID-19 Patients Using Gradient Boosting Decision Tree." International Journal of Biomaterials 2022 (September 16, 2022): 1–4. http://dx.doi.org/10.1155/2022/6474883.
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The aim of this paper is to predict the patient hospitalization time with coronavirus disease 2019 (COVID-19). It uses various data mining techniques, such as random forest. Many rules were derived by applying these techniques to the dataset. The extracted rules mainly were related to people over 55 years old. The rule with the most support states that if the person is between 70 and 80 years old, has cardiovascular disease, and the gender is female; then, the person will be hospitalized for at least five days. The gradient boosting random forest technique has performed better than other techniques. As a limitation of the study, it can be pointed out that a few features were unavailable and had not been recorded. Patients with diabetes, chronic respiratory problems, and cardiovascular diseases have a relatively long hospitalization. So, the hospital manager should consider a suitable priority for these patients. Older people were also more likely to take part in the selection rules.
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Yoon, Taeyoung, and Daesung Kang. "Multi-Modal Stacking Ensemble for the Diagnosis of Cardiovascular Diseases." Journal of Personalized Medicine 13, no. 2 (February 20, 2023): 373. http://dx.doi.org/10.3390/jpm13020373.
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Background: Cardiovascular diseases (CVDs) are a leading cause of death worldwide. Deep learning methods have been widely used in the field of medical image analysis and have shown promising results in the diagnosis of CVDs. Methods: Experiments were performed on 12-lead electrocardiogram (ECG) databases collected by Chapman University and Shaoxing People’s Hospital. The ECG signal of each lead was converted into a scalogram image and an ECG grayscale image and used to fine-tune the pretrained ResNet-50 model of each lead. The ResNet-50 model was used as a base learner for the stacking ensemble method. Logistic regression, support vector machine, random forest, and XGBoost were used as a meta learner by combining the predictions of the base learner. The study introduced a method called multi-modal stacking ensemble, which involves training a meta learner through a stacking ensemble that combines predictions from two modalities: scalogram images and ECG grayscale images. Results: The multi-modal stacking ensemble with a combination of ResNet-50 and logistic regression achieved an AUC of 0.995, an accuracy of 93.97%, a sensitivity of 0.940, a precision of 0.937, and an F1-score of 0.936, which are higher than those of LSTM, BiLSTM, individual base learners, simple averaging ensemble, and single-modal stacking ensemble methods. Conclusion: The proposed multi-modal stacking ensemble approach showed effectiveness for diagnosing CVDs.
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Wang, Zhizhong, Hongyi Li, Chuang Han, Songwei Wang, and Li Shi. "Arrhythmia Classification Based on Multiple Features Fusion and Random Forest Using ECG." Journal of Medical Imaging and Health Informatics 9, no. 8 (October 1, 2019): 1645–54. http://dx.doi.org/10.1166/jmihi.2019.2798.
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Cardiovascular diseases have become more and more prominent in recent years, which have proven to be a major threat to people's health. Accurate detection of arrhythmia in patients has important implications for clinical treatment. The aim of this study was to propose a novel automatic classification method for arrhythmia in order to improve classification accuracy. The electrocardiogram (ECG) signal was subjected preprocessing for denoising purposes using a wavelet transform. Then, the local and global characteristics of the beat, which contained RR interval features according with the clinical diagnosis criterion, morphology features based on wavelet packet decomposition and statistical features along with kurtosis coefficient, skewness coefficient and variance are exploited and fused. Meanwhile, the dimensionality of wavelet packet coefficients were reduced via principal component analysis (PCA). Finally, these features were used as the input of the random forest classifier to train the model and were then compared with the support vector machine (SVM) and back propagation (BP) neural networks. Based on 100,647 beats from the MIT-BIH database, the proposed method achieved an average accuracy, specificity and sensitivity of 99.08%, 99.00% and 89.31%, respectively, using the intra-patient beats, and 92.31%, 89.98% and 37.47%, respectively, using the inter-patient beats. Moreover, two classification schemes, namely, inter-patient and intra-patient scheme, were validated. Compared with the other methods referred to in this paper, the performance of the novel method yielded better results.
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Alqahtani, Abdullah, Shtwai Alsubai, Mohemmed Sha, Lucia Vilcekova, and Talha Javed. "Cardiovascular Disease Detection using Ensemble Learning." Computational Intelligence and Neuroscience 2022 (August 16, 2022): 1–9. http://dx.doi.org/10.1155/2022/5267498.
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One of the most challenging tasks for clinicians is detecting symptoms of cardiovascular disease as earlier as possible. Many individuals worldwide die each year from cardiovascular disease. Since heart disease is a major concern, it must be dealt with timely. Multiple variables affecting health, such as excessive blood pressure, elevated cholesterol, an irregular pulse rate, and many more, make it challenging to diagnose cardiac disease. Thus, artificial intelligence can be useful in identifying and treating diseases early on. This paper proposes an ensemble-based approach that uses machine learning (ML) and deep learning (DL) models to predict a person’s likelihood of developing cardiovascular disease. We employ six classification algorithms to predict cardiovascular disease. Models are trained using a publicly available dataset of cardiovascular disease cases. We use random forest (RF) to extract important cardiovascular disease features. The experiment results demonstrate that the ML ensemble model achieves the best disease prediction accuracy of 88.70%.
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Gulfam Ahmad, Hafiz, and Muhammad Jasim Shah. "PREDICTION OF CARDIOVASCULAR DISEASES (CVDS) USING MACHINE LEARNING TECHNIQUES IN HEALTH CARE CENTERS." Azerbaijan Journal of High Performance Computing 4, no. 2 (December 31, 2021): 267–79. http://dx.doi.org/10.32010/26166127.2021.4.2.267.279.
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Cardiovascular Diseases (CVDs) are one of the most common health problems nowadays. Early diagnosis of heart disease is a significant concern for health professionals in medical centers. An incorrect forecast is more likely to have negative effects, such as disability or even death. Our research is motivated by the desire to predict cardiovascular diseases based on data mining that can be valuable to medical centers. Various data mining approaches are used for the early detection of cardiac diseases. This paper examines several research publications that work on various heart diseases. We compare and contrast several machine learning methods, such as KNN, ANN, Decision Tree, SVM, and Random Forest. We looked at 918 observations with several features related to heart disease. A comparative study with age and sex is established to predict cardiac disease using the decision tree approach. Our dataset contains 11 features that are used to forecast possible heart disease. One of the attributes indicates that the age factor has the most significant impact on heart disease. According to our findings, heart attacks cause four out of every five CVD deaths, with one-third of these deaths occurring suddenly in those under 70.
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Dayana, Ms, K. Keerthika, E. Bibilin Manuela, and J. Julie Christina. "Prediction of Cardiovascular Disease Using PySpark Techniques." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 1228–33. http://dx.doi.org/10.22214/ijraset.2022.44018.
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Abstract: On a day after day, human life is affected by differing kinds of diseases that is why their life is in distress. cardiovascular disease may be a generic class of disease that's effective in spreading infections and notably, it affects the heart and veins. it's determined that vessel diseases have become modest in old individuals besides in children too. it's terribly requisite to portend this sort of illness within the starting phases; many varieties of tests square measure used for diagnosticating these ailments. This implementation has been done by employing a big data tool that's Apache Spark and victimization spark's MLlib and PySpark libraries that square measure integrated with it. Apache Spark is among the foremost wide used big data technologies, and it's a stack of some libraries that are Spark SQL, Spark MLlib, Spark Streaming, etc. This analysis work aims to create a prediction model to predict whether or not people have cardiovascular disease or not, using machine learning classification techniques that embrace logistic regression, decision tree, random forest to enhance the performance of models. They compared the analysis of all applied machine learning models. The results obtained are compared with the results of existing models within the same domain and located to be improved. Keywords: heart, blood vessels, Xampp server, data analytics, cardiovascular diseases.
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Moskaleva, Natalia E., Ksenia M. Shestakova, Alexey V. Kukharenko, Pavel A. Markin, Maria V. Kozhevnikova, Ekaterina O. Korobkova, Alex Brito, et al. "Target Metabolome Profiling-Based Machine Learning as a Diagnostic Approach for Cardiovascular Diseases in Adults." Metabolites 12, no. 12 (November 27, 2022): 1185. http://dx.doi.org/10.3390/metabo12121185.
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Metabolomics is a promising technology for the application of translational medicine to cardiovascular risk. Here, we applied a liquid chromatography/tandem mass spectrometry approach to explore the associations between plasma concentrations of amino acids, methylarginines, acylcarnitines, and tryptophan catabolism metabolites and cardiometabolic risk factors in patients diagnosed with arterial hypertension (HTA) (n = 61), coronary artery disease (CAD) (n = 48), and non-cardiovascular disease (CVD) individuals (n = 27). In total, almost all significantly different acylcarnitines, amino acids, methylarginines, and intermediates of the kynurenic and indolic tryptophan conversion pathways presented increased (p < 0.05) in concentration levels during the progression of CVD, indicating an association of inflammation, mitochondrial imbalance, and oxidative stress with early stages of CVD. Additionally, the random forest algorithm was found to have the highest prediction power in multiclass and binary classification patients with CAD, HTA, and non-CVD individuals and globally between CVD and non-CVD individuals (accuracy equal to 0.80 and 0.91, respectively). Thus, the present study provided a complex approach for the risk stratification of patients with CAD, patients with HTA, and non-CVD individuals using targeted metabolomics profiling.
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Arsyan, Athalla Rizky, and Wikky Fawwaz Al Maki. "Classification of Glaucoma Using Invariant Moment Methods on K-Nearest Neighbor and Random Forest Models." Building of Informatics, Technology and Science (BITS) 3, no. 4 (March 31, 2022): 466–72. http://dx.doi.org/10.47065/bits.v3i4.1244.
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One of the cardiovascular diseases that can interfere with eye vision is glaucoma. This disease is caused by high pressure on the inside of the eyeball to cause blindness slowly. In general, screening or early diagnosis can help prevent glaucoma, specifically by analyzing several eye components affected by pressure, including the optical disc, optical cup, and blood vessels. Thus, by blending machine learning algorithms and computer vision technology, glaucoma classification and identification can be accelerated and improved. This study applied the Invariant Moment method to extract the optical cup and blood vessel segmentation's shape, scale, and rotation features. To obtain segmentation results for these two objects, we threshold two image datasets, DrishtiGS-1 and REFUGE, and implemented the approach described in this study to analyze system performance on these datasets. For the classification method used in this study, we proposed KNN and RF models to evaluate the suitability of the methods we used on the REFUGE and DrishtiGS-1 datasets and demonstrated that both models could be used to identify glaucoma through the use of fundus images. When the datasets were merged, we obtained 81.86% and 75.86% of accuracy when using blood vessel and optical cup segmentation results, respectively
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Harami, Roya Vaziri, Pegah Seif, Ali Kheradmand, and Saharnaz Vaziri Harami. "The Role of Sleep Quality and Mental Health in Cardiovascular Disease." Pakistan Journal of Medical and Health Sciences 15, no. 7 (July 30, 2021): 2082–86. http://dx.doi.org/10.53350/pjmhs211572082.
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Objectives: Cardiovascular diseases (CVDs) may accompany other diseases. Of which can name sleep disorders and also other psychiatric disorders. Aim: In current study we evaluate the concomitant mental health disorders and the sleep quality among the acute myocardial infarction (AMI) and acute coronary syndrome (ACS) patients. Method:180 cases were selected through random sampling. 90 of the cases were hospitalized because of acute myocardial AMI and 90 patients were admitted with the diagnosis of ACS. Demographic, GHQ 28 and PSQI questionnaire was applied to evaluate the demographic features, psychological wellbeing and sleep quality subsequently. Results: 57.8% of cases were women 42.2% were men. The age range was 27 75 years old and the mean age was 49.93+11.73years old. 87.8% of the patients were married and rest were single. The mean score for the GHQ 28 questionnaire was22.43+10.99in patients with angina and in AMI patients.38.8% of ACS patients and 50% of AMI patients didn’t feel well psychologically. The mean score for sleep quality in ACS patients was 3.08+3.6 and 4.06+3.8 in AMI patients. 32.3% of ACS cases and 24.4% of AMI cases had troubles in sleeping. Conclusion: The mental health disorders prevail in AMI and ACS patients. Furthermore, the poor sleep quality was correlated with mental health disorders. Keywords: Sleep Wake Disorders, Mental Health, Acute Coronary Syndrome, Myocardial Infarction
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Kim, Joung Ouk (Ryan), Yong-Suk Jeong, Jin Ho Kim, Jong-Weon Lee, Dougho Park, and Hyoung-Seop Kim. "Machine Learning-Based Cardiovascular Disease Prediction Model: A Cohort Study on the Korean National Health Insurance Service Health Screening Database." Diagnostics 11, no. 6 (May 25, 2021): 943. http://dx.doi.org/10.3390/diagnostics11060943.
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Background: This study proposes a cardiovascular diseases (CVD) prediction model using machine learning (ML) algorithms based on the National Health Insurance Service-Health Screening datasets. Methods: We extracted 4699 patients aged over 45 as the CVD group, diagnosed according to the international classification of diseases system (I20–I25). In addition, 4699 random subjects without CVD diagnosis were enrolled as a non-CVD group. Both groups were matched by age and gender. Various ML algorithms were applied to perform CVD prediction; then, the performances of all the prediction models were compared. Results: The extreme gradient boosting, gradient boosting, and random forest algorithms exhibited the best average prediction accuracy (area under receiver operating characteristic curve (AUROC): 0.812, 0.812, and 0.811, respectively) among all algorithms validated in this study. Based on AUROC, the ML algorithms improved the CVD prediction performance, compared to previously proposed prediction models. Preexisting CVD history was the most important factor contributing to the accuracy of the prediction model, followed by total cholesterol, low-density lipoprotein cholesterol, waist-height ratio, and body mass index. Conclusions: Our results indicate that the proposed health screening dataset-based CVD prediction model using ML algorithms is readily applicable, produces validated results and outperforms the previous CVD prediction models.
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Parajuli, Samikshya, and Tulsi Ram Bhandari. "Prevalence of Risk Factors of Non-Communicable Diseases and Screening of Possible Cardiovascular Diseases among Adults in Devchuli Municipality of Nawalpur District, Nepal." Journal of Health and Allied Sciences 9, no. 2 (December 31, 2019): 14–18. http://dx.doi.org/10.37107/jhas.121.
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Introduction: The major Non-communicable diseases (NCDs) are cardiovascular diseases, cancer, diabetes, and chronic respiratory diseases. Among the cardiovascular diseases, myocardial infarction and angina have high morbidity and mortality worldwide. This study assessed the prevalence of risk factors of NCD among adults, screened possible CVDs (myocardial infraction and angina) among adults and related presence of risk factors with possible CVDs (myocardial infarction and angina)
Methods: A cross-sectional study was conducted among an adult population of 30 to 50 years in Devchuli Municipality of Nawalpur district, Nepal from June to December 2018. We used WHO STEPS survey questionnaire to assess risk factors and Rose Angina Questionnaire given by WHO to find out the possible myocardial infarction and angina as study tools. We used complete filled 372 questionnaires to analyze and draw the results. Proportionate random sampling was performed to obtain the sample from each ward.
Results: The prevalence of the use of any type of tobacco products was 20.7% and consumption of alcoholic products was 19.62%. Prevalence of physically inactive was found at 44.9%. Out of the total participants 25%, 15.3%, and 3.5% reported hypertension, diabetes, and cardiovascular diseases respectively. Prevalence of possible rose angina and the myocardial infarction was found to be 8.06% and 2.7% respectively. Smoking (χ2=9.685, df=1, p=0.02) and alcohol consumption (χ2=4.331, df=1, p=0.037) were found significantly associated with Rose Angina.
Conclusions: The prevalence of risk factors of non-communicable diseases was high. Possible angina and the myocardial infarction were also found out in the adult population. Individual and community-based behavior change intervention program would be the way out to overcome the problem.
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Hanumegowda, Pradeep Kumar, and Sakthivel Gnanasekaran. "Prediction of Work-Related Risk Factors among Bus Drivers Using Machine Learning." International Journal of Environmental Research and Public Health 19, no. 22 (November 17, 2022): 15179. http://dx.doi.org/10.3390/ijerph192215179.
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A recent development in ergonomics research is using machine learning techniques for risk assessment and injury prevention. Bus drivers are more likely than other workers to suffer musculoskeletal diseases because of the nature of their jobs and their working conditions (WMSDs). The basic idea of this study is to forecast important work-related risk variables linked to WMSDs in bus drivers using machine learning approaches. A total of 400 full-time male bus drivers from the east and west zone depots of Bengaluru Metropolitan Transport Corporation (BMTC), which is based in Bengaluru, south India, took part in this study. In total, 92.5% of participants responded to the questionnaire. The Modified Nordic Musculoskeletal Questionnaire was used to gather data on symptoms of WMSD during the past 12 months (MNMQ). Machine learning techniques including decision tree, random forest, and naïve Bayes were used to forecast the important risk factors related to WMSDs. It was discovered that WMSDs and work-related characteristics were statistically significant. In total, 66.75% of subjects reported having WMSDs. Various classifiers were used to derive the simulation results for the frequency of pain in the musculoskeletal systems throughout the last 12 months with the important risk variables. With 100% accuracy, decision tree and random forest algorithms produce the same results. Naïve Bayes yields 93.28% accuracy. In this study, through a questionnaire survey and data analysis, several health and work-related risk factors were identified among the bus drivers. Risk factors such as involvement in physical activities, frequent posture change, exposure to vibration, egress ingress, on-duty breaks, and seat adaptability issues have the highest influence on the frequency of pain due to WMSDs among bus drivers. From this study, it is recommended that drivers get involved in physical activities, adopt a healthy lifestyle, and maintain proper posture while driving. For any transport organization/company, it is recommended to design driver cabins ergonomically to mitigate the WMSDs among bus drivers.
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Hu, Wei-Syun, Meng-Hsuen Hsieh, and Cheng-Li Lin. "A novel atrial fibrillation prediction model for Chinese subjects: a nationwide cohort investigation of 682 237 study participants with random forest model." EP Europace 21, no. 9 (April 23, 2019): 1307–12. http://dx.doi.org/10.1093/europace/euz036.
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Abstract Aims We aimed to construct a random forest model to predict atrial fibrillation (AF) in Chinese population. Methods and results This study was comprised of 682 237 subjects with or without AF. Each subject had 19 features that included the subjects’ age, gender, underlying diseases, CHA2DS2-VASc score, and follow-up period. The data were split into train and test sets at an approximate 9:1 ratio: 614 013 data points were placed into the train set and 68 224 data points were placed into the test set. In this study, weighted average F1, precision, and recall values were used to measure prediction model performance. The F1, precision, and recall values were calculated across the train set, the test set, and all data. The area under receiving operating characteristic (ROC) curve was also used to evaluate the performance of the prediction model. The prediction model achieved a k-fold cross-validation accuracy of 0.979 (k = 10). In the test set, the prediction model achieved an F1 value of 0.968, precision value of 0.958, and recall value of 0.979. The area under ROC curve of the model was 0.948 (95% confidence interval 0.947–0.949). This model was validated with a separate dataset. Conclusions This study showed a novel AF risk prediction scheme for Chinese individuals with random forest model methodology.
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Nirmala, M., and V. Saravanan. "Clinical Implication of Machine Learning Based Cardiovascular Disease Prediction Using IBM Auto AI Service." International Journal for Research in Applied Science and Engineering Technology 10, no. 8 (August 31, 2022): 124–44. http://dx.doi.org/10.22214/ijraset.2022.46087.
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Abstract: Cardio vascular diseases are the number one cause of death globally, taking an estimated 17.9 million lives each year, which accounts for 31% of all deaths worldwide. Heart failure is a common event caused by CVDs and this dataset contains 11 features that can be used to predict mortality by heart failure. In this project, a model is built using the Random Forest Classifier Algorithm using AutoAI and a web application is created using Node Red Application and it showcases the prediction of heart failure in a web based format. The usage of IBM cloud environment for implementing the Machine learning Model using IBM Auto AI and and Node Red Flows are created for the display of Web Application Structure. The complete project explains the coordination among the Auto AI and Node red in the Cloud Platform.
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Morell Miranda, Pedro, Francesca Bertolini, and Haja N. Kadarmideen. "Investigation of gut microbiome association with inflammatory bowel disease and depression: a machine learning approach." F1000Research 7 (June 5, 2018): 702. http://dx.doi.org/10.12688/f1000research.15091.1.
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Background: Inflammatory bowel disease (IBD) is a group of chronic diseases related to inflammatory processes in the digestive tract generally associated with an immune response to an altered gut microbiome in genetically predisposed subjects. For years, both researchers and clinicians have been reporting increased rates of anxiety and depression disorders in IBD, and these disorders have also been linked to an altered microbiome. However, the underlying pathophysiological mechanisms of comorbidity are poorly understood at the gut microbiome level. Methods: Metagenomic and metatranscriptomic data were retrieved from the Inflammatory Bowel Disease Multi-Omics Database. Samples from 70 individuals that had answered to a self-reported depression and anxiety questionnaire were selected and classified by their IBD diagnosis and their questionnaire results, creating six different groups. The cross-validation random forest algorithm was used in 90% of the individuals (training set) to retain the most important species involved in discriminating the samples without losing predictive power. The validation set that represented the remaining 10% of the samples equally distributed across the six groups was used to train a random forest using only the species selected in order to evaluate their predictive power. Results: A total of 24 species were identified as the most informative in discriminating the 6 groups. Several of these species were frequently described in dysbiosis cases, such as species from the genus Bacteroides and Faecalibacterium prausnitzii. Despite the different compositions among the groups, no common patterns were found between samples classified as depressed. However, distinct taxonomic profiles within patients of IBD depending on their depression status were detected. Conclusions: The machine learning approach is a promising approach for investigating the role of microbiome in IBD and depression. Abundance and functional changes in these species suggest that depression should be considered as a factor in future research on IBD.
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Morell Miranda, Pedro, Francesca Bertolini, and Haja N. Kadarmideen. "Investigation of gut microbiome association with inflammatory bowel disease and depression: a machine learning approach." F1000Research 7 (April 17, 2019): 702. http://dx.doi.org/10.12688/f1000research.15091.2.
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Background: Inflammatory bowel disease (IBD) is a group of chronic diseases related to inflammatory processes in the digestive tract generally associated with an immune response to an altered gut microbiome in genetically predisposed subjects. For years, both researchers and clinicians have been reporting increased rates of anxiety and depression disorders in IBD, and these disorders have also been linked to an altered microbiome. However, the underlying pathophysiological mechanisms of comorbidity are poorly understood at the gut microbiome level. Methods: Metagenomic and metatranscriptomic data were retrieved from the Inflammatory Bowel Disease Multi-Omics Database. Samples from 70 individuals that had answered to a self-reported depression and anxiety questionnaire were selected and classified by their IBD diagnosis and their questionnaire results, creating six different groups. The cross-validation random forest algorithm was used in 90% of the individuals (training set) to retain the most important species involved in discriminating the samples without losing predictive power. The validation set that represented the remaining 10% of the samples equally distributed across the six groups was used to train a random forest using only the species selected in order to evaluate their predictive power. Results: A total of 24 species were identified as the most informative in discriminating the 6 groups. Several of these species were frequently described in dysbiosis cases, such as species from the genus Bacteroides and Faecalibacterium prausnitzii. Despite the different compositions among the groups, no common patterns were found between samples classified as depressed. However, distinct taxonomic profiles within patients of IBD depending on their depression status were detected. Conclusions: The machine learning approach is a promising approach for investigating the role of microbiome in IBD and depression. Abundance and functional changes in these species suggest that depression should be considered as a factor in future research on IBD.
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Stavem, Knut, Henrik Schirmer, and Amund Gulsvik. "Respiratory symptoms and cardiovascular causes of deaths: A population-based study with 45 years of follow-up." PLOS ONE 17, no. 10 (October 20, 2022): e0276560. http://dx.doi.org/10.1371/journal.pone.0276560.
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This study determined the association between respiratory symptoms and death from cardiovascular (CV) diseases during 45 years in a pooled sample of four cohorts of random samples of the Norwegian population with 95,704 participants. Respiratory symptoms were assessed using a modification of the MRC questionnaire on chronic bronchitis. We analyzed the association between respiratory symptoms and specific cardiovascular deaths by using Cox regression analysis with age as the time variable, accounting for cluster-specific random effects using shared frailty for study cohort. Hazard ratios (HR) for death were adjusted for sex, highest attained education, smoking habits, occupational air pollution, and birth cohort. Overall, 12,491 (13%) of participants died from CV diseases: 4,123 (33%) acute myocardial infarction, 2,326 (18%) other ischemic heart disease, 2,246 (18%) other heart diseases, 2,553 (20%) cerebrovascular diseases, and 1,120 (9%) other vascular diseases. The adjusted HR (95% confidence interval) for CV deaths was 1.9 (1.7–2.1) in men and 1.5 (1.2–1.9) in women for “yes” to the question “Are you breathless when you walk on level ground at an ordinary pace?”. The same item response showed an adjusted HR for death from acute myocardial infarction of 1.8 (1.5–2.1), other ischemic heart disease 2.2 (1.8–2.7), other heart diseases 1.5 (1.1–1.9), cerebrovascular disease 1.8 (1.5–2.3), and other circulatory diseases 1.7 (1.2–2.4). The adjusted HR for CV death was 1.3 (1.2–1.4) when answering positive to the question” Are you more breathless than people of your own age when walking uphill?”. However, positive answers to questions on cough, phlegm, wheezing and attacks of breathlessness were after adjustments not associated with early CV deaths. The associations between CV deaths and breathlessness were also present in never smokers. Self-reported breathlessness was associated with CV deaths and could be an early marker of CV deaths.
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RIYAZ, Lubna, Muheet Ahmed BUTT, and Majid ZAMAN. "IMPROVING CORONARY HEART DISEASE PREDICTION BY OUTLIER ELIMINATION." Applied Computer Science 18, no. 1 (March 30, 2022): 70–88. http://dx.doi.org/10.35784/acs-2022-6.
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Nowadays, heart disease is the major cause of deaths globally. According to a survey conducted by the World Health Organization, almost 18 million people die of heart diseases (or cardiovascular diseases) every day. So, there should be a system for early detection and prevention of heart disease. Detection of heart disease mostly depends on the huge pathological and clinical data that is quite complex. So, researchers and other medical professionals are showing keen interest in accurate prediction of heart disease. Heart disease is a general term for a large number of medical conditions related to heart and one of them is the coronary heart disease (CHD). Coronary heart disease is caused by the amassing of plaque on the artery walls. In this paper, various machine learning base and ensemble classifiers have been applied on heart disease dataset for efficient prediction of coronary heart disease. Various machine learning classifiers that have been employed include k-nearest neighbor, multilayer perceptron, multinomial naïve bayes, logistic regression, decision tree, random forest and support vector machine classifiers. Ensemble classifiers that have been used include majority voting, weighted average, bagging and boosting classifiers. The dataset used in this study is obtained from the Framingham Heart Study which is a long-term, ongoing cardiovascular study of people from the Framingham city in Massachusetts, USA. To evaluate the performance of the classifiers, various evaluation metrics including accuracy, precision, recall and f1 score have been used. According to our results, the best accuracy was achieved by logistic regression, random forest, majority voting, weighted average and bagging classifiers but the highest accuracy among these was achieved using weighted average ensemble classifier.
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Nastenko, Ievgen, Vitaliy Maksymenko, Sergiy Potashev, Volodymyr Pavlov, Vitalii Babenko, Sergiy Rysin, Oleksandr Matviichuk, and Vasil Lazoryshinets. "Random Forest Algorithm Construction for the Diagnosis of Coronary Heart Disease Based on Echocardiography Video Data Streams." Innovative Biosystems and Bioengineering 5, no. 1 (April 6, 2021): 61–69. http://dx.doi.org/10.20535/ibb.2021.5.1.225794.
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Background. Recent studies show that cardiovascular diseases, including coronary heart disease, are the leading causes of death and one of the main factors of disability worldwide. The detection of cases of this type of disease over the past 30 years has increased from 271 million to 523 million and the number of deaths – from 12.1 million to 18.6 million. Cardiovascular diseases are the main cause of death among the population of Ukraine and, according to this indicator, the country remains one of the world leaders. Coronary heart disease is the leading factor in the loss of health in Ukraine and modern diagnostic methods, including machine learning algorithms, are increasingly being used for timely detection. Objective. According to the data of speckle-tracking echocardiography using the random forest method, construct classification algorithms for diagnosing violations of the kinematics of left ventricular contractions in patients with coronary heart disease at rest, and when using an echostress test with a dobutamine test. Methods. Speckle-tracking echocardiography was used to examine 40 patients with coronary heart disease and 16 in whom no cardiac pathology was found. Echocardiography was recorded in B mode in three positions: along the long axis, in 4-chamber, and 2-chamber positions. In total, 6245 frames of the video stream were used: 1871 – without cardiac abnormalities, and 4374 – in the presence of pathology during the examination. 56 patients (2509 frames of video data) were examined without the use of a dobutamine test and 38 patients (3736 frames of video data) – using an echostress test with a dobutamine test if no disturbances were found at rest. Dobutamine doses of 10, 20, and 40 mcg were administered under the supervision of an anesthesiologist. The data of texture analysis of images were used as informative features. To build an algorithm for detecting coronary heart disease the random forest algorithm was applied. Results. At the first stage of the study, the diagnostic algorithms norma–pathology for the state of rest and dobutamine doses of 10, 20, and 40 mcg were constructed. Before applying the algorithm the samples were randomly divided into training (70%) and test (30%). The classifiers were evaluated for accuracy, sensitivity, and specificity. According to the test samples, the accuracy of diagnostic conclusions varied from 97 to 99%. At the second stage of the study, to increase the versatility of the models, the classifier was built for all images, without dividing them into dobutamine doses. The accuracy for the test samples also ranged from 96.6 to 97.8%. To construct diagnostic algorithms by the random forest method the data of texture analysis of images were used. Conclusions. High-precision classification models were obtained using the random forest algorithm. The developed models can be applied to the analysis of echocardiograms obtained in B mode on equipment that is not equipped with the speckle tracking technology.
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Imran Farooqui, Sumaira, Amna Aamir Khan, Aqsa Sajjad, Kinza Mannal, and Farzana Amir. "PREVALENCE OF PERIPHERAL ARTERIAL DISEASE (PAD) ASSOCIATED WITH FAST FOOD CONSUMPTION, USING ANKLE- BRACHIAL INDEX IN UNIVERSITY STUDENTS." Pakistan Journal of Rehabilitation 4, no. 2 (July 1, 2015): 7–14. http://dx.doi.org/10.36283/pjr.zu.4.2/004.
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OBJECTIVE The objective of this study is to find out the prevalence of PAD associated with fast food consumptions in university students under the age of 18-25 years. STUDY DESIGN Observational study SAMPLING TECHNIQUE Convenience random sampling SAMPLE SIZE 57 students with age range of 18-25 years OUTCOME MEASURE Ankle Brachial Index (ABI) METHODOLOGY The subjects were recruited from first year to final year students for the study. A one month dietary questionnaire was given to the students. We instructed the students to fill questionnaire on daily basis. The dietary questionnaire contained list of 46 fast food items. After a month, a portable Doppler Ultrasound was used to measure their ABI values. RESULTS Fast food consumption highly impacts on peripheral arterial disease (p 0.00) so the association of fast food with PAD predicts the upcoming cardiovascular events in university students. There is no significant relationship of gender with peripheral arterial diseases (p 0.335). Also, there is no significance relation between fast food and gender (p 0.153). CONCLUSION In this territory, the PAD is increasing particularly in the ages of 18-25 years and all individuals with an ABI <1.0 demonstrates as a minimum one classical cardiovascular risk factor, which needs sufficient concentration and an aggressive risk management. Keywords: Ankle Brachial Pressure index, Peripheral Arterial Diseases, Fast Food Consumption, University Students, Cardiovascular Disease, Coronary Heart Disease, Atherosclerosis
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El Massari, Hakim, Noreddine Gherabi, Sajida Mhammedi, Hamza Ghandi, Mohamed Bahaj, and Muhammad Raza Naqvi. "The Impact of Ontology on the Prediction of Cardiovascular Disease Compared to Machine Learning Algorithms." International Journal of Online and Biomedical Engineering (iJOE) 18, no. 11 (August 31, 2022): 143–57. http://dx.doi.org/10.3991/ijoe.v18i11.32647.
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Cardiovascular disease is one of the chronic diseases that is on the rise. The complications occur when cardiovascular disease is not discovered early and correctly diagnosed at the right time. Various machine learning approaches, including ontology-based Machine Learning techniques, have lately played an essential role in medical science by building an automated system that can identify heart illness. This paper compares and reviews the most prominent machine learning algorithms, as well as ontology-based Machine Learning classification. Random Forest, Logistic regression, Decision Tree, Naive Bayes, k-Nearest Neighbours, Artificial Neural Network, and Support Vector Machine were among the classification methods explored. The dataset used consists of 70000 instances and can be downloaded from the Kaggle website. The findings are assessed using performance measures generated from the confusion matrix, such as F-Measure, Accuracy, Recall, and Precision. The results showed that the ontology outperformed all the machine learning algorithms.
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Baashar, Yahia, Gamal Alkawsi, Hitham Alhussian, Luiz Fernando Capretz, Ayed Alwadain, Ammar Ahmed Alkahtani, and Malek Almomani. "Effectiveness of Artificial Intelligence Models for Cardiovascular Disease Prediction: Network Meta-Analysis." Computational Intelligence and Neuroscience 2022 (February 24, 2022): 1–12. http://dx.doi.org/10.1155/2022/5849995.
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Heart failure is the most common cause of death in both males and females around the world. Cardiovascular diseases (CVDs), in particular, are the main cause of death worldwide, accounting for 30% of all fatalities in the United States and 45% in Europe. Artificial intelligence (AI) approaches such as machine learning (ML) and deep learning (DL) models are playing an important role in the advancement of heart failure therapy. The main objective of this study was to perform a network meta-analysis of patients with heart failure, stroke, hypertension, and diabetes by comparing the ML and DL models. A comprehensive search of five electronic databases was performed using ScienceDirect, EMBASE, PubMed, Web of Science, and IEEE Xplore. The search strategy was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement. The methodological quality of studies was assessed by following the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) guidelines. The random-effects network meta-analysis forest plot with categorical data was used, as were subgroups testing for all four types of treatments and calculating odds ratio (OR) with a 95% confidence interval (CI). Pooled network forest, funnel plots, and the league table, which show the best algorithms for each outcome, were analyzed. Seventeen studies, with a total of 285,213 patients with CVDs, were included in the network meta-analysis. The statistical evidence indicated that the DL algorithms performed well in the prediction of heart failure with AUC of 0.843 and CI [0.840–0.845], while in the ML algorithm, the gradient boosting machine (GBM) achieved an average accuracy of 91.10% in predicting heart failure. An artificial neural network (ANN) performed well in the prediction of diabetes with an OR and CI of 0.0905 [0.0489; 0.1673]. Support vector machine (SVM) performed better for the prediction of stroke with OR and CI of 25.0801 [11.4824; 54.7803]. Random forest (RF) results performed well in the prediction of hypertension with OR and CI of 10.8527 [4.7434; 24.8305]. The findings of this work suggest that the DL models can effectively advance the prediction of and knowledge about heart failure, but there is a lack of literature regarding DL methods in the field of CVDs. As a result, more DL models should be applied in this field. To confirm our findings, more meta-analysis (e.g., Bayesian network) and thorough research with a larger number of patients are encouraged.
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V. Ramalingam, V., Ayantan Dandapath, and M. Karthik Raja. "Heart disease prediction using machine learning techniques : a survey." International Journal of Engineering & Technology 7, no. 2.8 (March 19, 2018): 684. http://dx.doi.org/10.14419/ijet.v7i2.8.10557.
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Heart related diseases or Cardiovascular Diseases (CVDs) are the main reason for a huge number of death in the world over the last few decades and has emerged as the most life-threatening disease, not only in India but in the whole world. So, there is a need of reliable, accurate and feasible system to diagnose such diseases in time for proper treatment. Machine Learning algorithms and techniques have been applied to various medical datasets to automate the analysis of large and complex data. Many researchers, in recent times, have been using several machine learning techniques to help the health care industry and the professionals in the diagnosis of heart related diseases. This paper presents a survey of various models based on such algorithms and techniques andanalyze their performance. Models based on supervised learning algorithms such as Support Vector Machines (SVM), K-Nearest Neighbour (KNN), NaïveBayes, Decision Trees (DT), Random Forest (RF) and ensemble models are found very popular among the researchers.
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Dubey, A. K., A. K. Sinhal, and R. Sharma. "An Improved Auto Categorical PSO with ML for Heart Disease Prediction." Engineering, Technology & Applied Science Research 12, no. 3 (June 6, 2022): 8567–73. http://dx.doi.org/10.48084/etasr.4854.
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Cardiovascular or heart diseases consist a global major health concern. Cardiovascular diseases have the highest mortality rate worldwide, and the death rate increases with age, but an accurate prognosis at an early stage may increase the chances of surviving. In this paper, a combined approach, based on Machine Learning (ML) with an optimization method for the prediction of heart diseases is proposed. For this, the Improved Auto Categorical Particle Swarm Optimization (IACPSO) method was utilized to pick an optimum set of features, while ML methods were used for data categorization. Three heart disease datasets were taken from the UCI ML library for testing: Cleveland, Statlog, and Hungarian. The proposed model was assessed for different performance parameters. The results indicated that, with 98% accuracy, Logistic Regression (LR) and Support Vector Machine by Grid Search (SVMGS) performed better for the Statlog, SVMGS outperformed on the Cleveland, while the LR, Random Forest (RF), Support Vector Machine (SVM), and SVMGS performed better with 97% accuracy on the Hungarian dataset. The outcomes were improved by 3 to 33% in terms of performance parameters when ML was applied with IACPSO.
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Hossen, M. D. Amzad, Tahia Tazin, Sumiaya Khan, Evan Alam, Hossain Ahmed Sojib, Mohammad Monirujjaman Khan, and Abdulmajeed Alsufyani. "Supervised Machine Learning-Based Cardiovascular Disease Analysis and Prediction." Mathematical Problems in Engineering 2021 (December 10, 2021): 1–10. http://dx.doi.org/10.1155/2021/1792201.
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Cardiovascular illness, often commonly known as heart disease, encompasses a variety of diseases that affect the heart and has been the leading cause of mortality globally in recent decades. It is associated with numerous risks for heart disease and a requirement of the moment to get accurate, trustworthy, and reasonable methods to establish an early diagnosis in order to accomplish early disease treatment. In the healthcare sector, data analysis is a widely utilized method for processing massive amounts of data. Researchers use a variety of statistical and machine learning methods to evaluate massive amounts of complicated medical data, assisting healthcare practitioners in predicting cardiac disease. This study covers many aspects of cardiac illness, as well as a model based on supervised learning techniques such as Random Forest (RF), Decision Tree (DT), and Logistic Regression (LR). It makes use of an existing dataset from the UCI Cleveland database of heart disease patients. There are 303 occurrences and 76 characteristics in the collection. Only 14 of these 76 characteristics are evaluated for testing, which is necessary to validate the performance of various methods. The purpose of this study is to forecast the likelihood of individuals getting heart disease. The findings show that logistic regression achieves the best accuracy score (92.10%).
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Mayya, A., and H. Solieman. "Machine Learning System for Predicting Cardiovascular Disorders in Diabetic Patients." Journal of the Russian Universities. Radioelectronics 25, no. 4 (September 29, 2022): 116–22. http://dx.doi.org/10.32603/1993-8985-2022-25-4-116-122.
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Introduction. Patients with diabetes are exposed to various cardiovascular risk factors, which lead to an increased risk of cardiac complications. Therefore, the development of a diagnostic system for diabetes and cardiovascular disease (CVD) is a relevant research task. In addition, the identification of the most significant indicators of both diseases may help physicians improve treatment, speed the diagnosis, and decrease its computational costs.Aim. To classify subjects with different diabetes types, predict the risk of cardiovascular diseases in diabetic patients using machine learning methods by finding the correlational indicators.Materials and methods. The NHANES database was used following preprocessing and balancing its data. Machine learning methods were used to classify diabetes based on physical examination data and laboratory data. Feature selection methods were used to derive the most significant indicators for predicting CVD risk in diabetic patients. Performance optimization of the developed classification and prediction models was carried out based on different evaluation metrics.Results. The developed model (Random Forest) achieved the accuracy of 93.1 % (based on laboratory data) and 88 % (based on pysicical examination plus laboratory data). The top five most common predictors in diabetes and prediabetes were found to be glycohemoglobin, basophil count, triglyceride level, waist size, and body mass index (BMI). These results seem logical, since glycohemoglobin is commonly used to check the amount of glucose (sugar) bound to the hemoglobin in the red blood cells. For CVD patients, the most common predictors inlcude eosinophil count (indicative of blood diseases), gamma-glutamyl transferase (GGT), glycohemoglobin, overall oral health, and hand stiffness.Conclusion. Balancing the dataset and deleting NaN values improved the performance of the developed models. The RFC and XGBoost models achieved higher accuracy using gradient descending order to minimize the loss function. The final prediction is made using a weighted majority vote of all the decisions. The result was an automated system for predicting CVD risk in diabetic patients.
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Khan, Arsalan, Moiz Qureshi, Muhammad Daniyal, and Kassim Tawiah. "A Novel Study on Machine Learning Algorithm-Based Cardiovascular Disease Prediction." Health & Social Care in the Community 2023 (February 20, 2023): 1–10. http://dx.doi.org/10.1155/2023/1406060.
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Cardiovascular disease (CVD) is a life-threatening disease rising considerably in the world. Early detection and prediction of CVD as well as other heart diseases might protect many lives. This requires tact clinical data analysis. The potential of predictive machine learning algorithms to develop the doctor’s perception is essential to all stakeholders in the health sector since it can augment the efforts of doctors to have a healthier climate for patient diagnosis and treatment. We used the machine learning (ML) algorithm to carry out a significant explanation for accurate prediction and decision making for CVD patients. Simple random sampling was used to select heart disease patients from the Khyber Teaching Hospital and Lady Reading Hospital, Pakistan. ML methods such as decision tree (DT), random forest (RF), logistic regression (LR), Naïve Bayes (NB), and support vector machine (SVM) were implemented for classification and prediction purposes for CVD patients in Pakistan. We performed exploratory analysis and experimental output analysis for all algorithms. We also estimated the confusion matrix and recursive operating characteristic curve for all algorithms. The performance of the proposed ML algorithm was estimated using numerous conditions to recognize the best suitable machine learning algorithm in the class of models. The RF algorithm had the highest accuracy of prediction, sensitivity, and recursive operative characteristic curve of 85.01%, 92.11%, and 87.73%, respectively, for CVD. It also had the least specificity and misclassification errors of 43.48% and 8.70%, respectively, for CVD. These results indicated that the RF algorithm is the most appropriate algorithm for CVD classification and prediction. Our proposed model can be implemented in all settings worldwide in the health sector for disease classification and prediction.
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Chumachenko, Dmytro, Mykola Butkevych, Daniel Lode, Marcus Frohme, Kurt J. G. Schmailzl, and Alina Nechyporenko. "Machine Learning Methods in Predicting Patients with Suspected Myocardial Infarction Based on Short-Time HRV Data." Sensors 22, no. 18 (September 17, 2022): 7033. http://dx.doi.org/10.3390/s22187033.
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Diagnosis of cardiovascular diseases is an urgent task because they are the main cause of death for 32% of the world’s population. Particularly relevant are automated diagnostics using machine learning methods in the digitalization of healthcare and introduction of personalized medicine in healthcare institutions, including at the individual level when designing smart houses. Therefore, this study aims to analyze short 10-s electrocardiogram measurements taken from 12 leads. In addition, the task is to classify patients with suspected myocardial infarction using machine learning methods. We have developed four models based on the k-nearest neighbor classifier, radial basis function, decision tree, and random forest to do this. An analysis of time parameters showed that the most significant parameters for diagnosing myocardial infraction are SDNN, BPM, and IBI. An experimental investigation was conducted on the data of the open PTB-XL dataset for patients with suspected myocardial infarction. The results showed that, according to the parameters of the short ECG, it is possible to classify patients with a suspected myocardial infraction as sick and healthy with high accuracy. The optimized Random Forest model showed the best performance with an accuracy of 99.63%, and a root mean absolute error is less than 0.004. The proposed novel approach can be used for patients who do not have other indicators of heart attacks.
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Jiang, Lili, Sirong Chen, Yuanhui Wu, Da Zhou, and Lihua Duan. "Prediction of coronary heart disease in gout patients using machine learning models." Mathematical Biosciences and Engineering 20, no. 3 (2022): 4574–91. http://dx.doi.org/10.3934/mbe.2023212.
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<abstract><p>Growing evidence shows that there is an increased risk of cardiovascular diseases among gout patients, especially coronary heart disease (CHD). Screening for CHD in gout patients based on simple clinical factors is still challenging. Here we aim to build a diagnostic model based on machine learning so as to avoid missed diagnoses or over exaggerated examinations as much as possible. Over 300 patient samples collected from Jiangxi Provincial People's Hospital were divided into two groups (gout and gout+CHD). The prediction of CHD in gout patients has thus been modeled as a binary classification problem. A total of eight clinical indicators were selected as features for machine learning classifiers. A combined sampling technique was used to overcome the imbalanced problem in the training dataset. Eight machine learning models were used including logistic regression, decision tree, ensemble learning models (random forest, XGBoost, LightGBM, GBDT), support vector machine (SVM) and neural networks. Our results showed that stepwise logistic regression and SVM achieved more excellent AUC values, while the random forest and XGBoost models achieved more excellent performances in terms of recall and accuracy. Furthermore, several high-risk factors were found to be effective indices in predicting CHD in gout patients, which provide insights into the clinical diagnosis.</p></abstract>
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Ashraf, M. Usman, Farwa Akram, and Sardar Usman. "Comparative Analysis of Machine Learning Techniques for Predicting Air Pollution." Lahore Garrison University Research Journal of Computer Science and Information Technology 6, no. 2 (June 26, 2022): 40–54. http://dx.doi.org/10.54692/lgurjcsit.2022.0602270.
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The modern and motorized way of life has cultured air pollution. Air pollution has become the biggest rival of robust living. This situation is becoming more lethal in developing countries and so in Pakistan. Hence, this inquiry was carried out to propose an architecture design that could make real-time prediction of air pollution with another purpose of scanning the frequently adopted algorithm in past investigations. In addition, it was also intended to narrate the toxic effects of air pollution on human health. So, this research was carried out on a large dataset of Seoul as an adequate dataset of Pakistan was not attainable. The dataset consisted of three years (2017-2019) including 647,512 instances and 11 attributes. The four distinctive algorithms termed Random Forest, Linear Regression, Decision Tree and XGBoosting were employed. It was inferred that XGB is more promising and feasible in predicting concentration level of NO2, O3, SO2, PM10, PM2.5 and CO with the lowest RMSE and MAE values of 0.0111, 0.0262, 0.0168, 49.64, 41.68 and 0.1856 and 0.0067, 0.0096, 0.0017, 12.28, 7.63 and 0.0982 respectively. Furthermore, it was found out as well that the Random Forest was preferred mostly in the previous studies related to air pollution prophecy while many probes supported that air pollution is very detrimental to human health especially long-lasting exposure causes lung cancer, respiratory and cardiovascular diseases.
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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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Tsygankova, D. P., E. B. Shapovalova, S. A. Maksimov, and G. V. Artamonova. "PROSPECTIVE STUDY OF DEVELOPMENT OF CARDIOVASCULAR EVENTS IN RELATION WITH CARDIOVASCULAR RISK (ESSE-RF IN KEMEROVSKAYA REGION)." Russian Journal of Cardiology, no. 6 (July 11, 2018): 141–46. http://dx.doi.org/10.15829/1560-4071-2018-6-141-146.
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Aim. Evaluation of cardiovascular risk factors influence on the development of cardiovascular events (hospitalization, coronary and carotid surgery, death) in prospective study.Material and methods. Analysis was performed by the results of multicenter epidemiologic study “Epidemiology of cardiovascular diseases and risk factors in Russian Federation (ESSE-RF)” in Kemerovskaya oblast, collected on a random sample of adult inhabitants, age 25-64 y. o. (n=1628). With the first screening, by interviewing, the data collected, on the main cardiovascular risk factors and objective data. In the prospective part of the study, in 4 years after primary screening, in participants and their families the data collected by phone interviewing, on cardiovascular events. Combinational endpoint was evaluated, by the class of cardiovascular pathology: death + hospitalization + surgery on coronary/carotid vessels. Combined influence of the studied factors was assessed with logistic regression.Results. Among the parameters of objective study, statistically significantly influenced the probability of combination endpoint development the age (OR=1,06 in 95% CI 1,02-1,09), male gender (OR=3,79 in 95% CI 1,88-7,61); influence of anamnesis of myocardial infarction is close to significant (OR=2,43 in 95% CI 0,73-8,10). Of the questionnaire-based parameters, significant influences on the probability of combinational endpoint development depression (OR=2,38 in 95% CI 1,32-4,29). Close to significant — absence no family status (OR=1,75 in 95% CI 0,98-3,13) and decreased life quality by EUROQOL-EQ-5D (OR=1,21 in 95% CI 0,98-1,49).Conclusion. A significant impact on the probability of cardiovascular diseases development during 4 year period of the study, do influence the age, male gender and depression.
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ALNohair, Sultan, Nahla Babiker, Dalal Al-Ahmari, Dalal Al-Mutairi, Khozama Al-Matroudi, Zakiyah Al-Mutairi, Rawan Al-Ahmdi, Layan Al-Mufadhi, Alhanouf Al-Wahiby, and Turki Alharbi. "Cross-sectional Study of Cardiovascular Risk Factors among Male and Female Medical Students in Qassim University – College of Medicine Saudi Arabia." Open Access Macedonian Journal of Medical Sciences 8, E (June 25, 2020): 439–45. http://dx.doi.org/10.3889/oamjms.2020.4501.
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BACKGROUND: Cardiovascular diseases (CVDs) are a major cause of mortality around the world. At present, almost half of the non-communicable diseases are CVDs. According to the literature review, CVD disease and the associated risk factors are high among Saudi adults. It has not been studied to determine at which age the majority of adults acquire the risk factors. We hypothesized and planned to assess CVD risk factors among medical students.
AIM: The main objective of this study is to determine the prevalence of CVD risk factors among male and female medical students in Qassim University.
METHODS: A cross-sectional study surveyed 188 males and female medical students in Qassim University. They were selected by random sampling technique. The data were collected by using a questionnaire included (age, gender, height, weight, waist circumference, blood pressure, random blood glucose, smoking habits, physical activity, and stress scale). After the data collection, it was entered and analyzed by SPSS.
RESULTS: About 9.6% of male students were smokers, while there is no history of smoking among female students. About 18.2% of males were found obese, while obesity was lower among females (4.2%). The random blood glucose for males and females was within normal limits, but the measured blood pressure showed a higher percentage of elevated blood pressure among males (47.8%) in comparison to females (25.4%). Perceived stress scale exhibited that females were getting a greater percentage of high stress (34.3%), while in males, it was 14.4%.
CONCLUSION: Many risk factors were greater among males, including elevated blood pressure 47.8%, obesity 18.2%, and smoking 9.6%. On the other hand, these risk factors were lower in females, but they have a higher stress scale 34.3% in comparison to males.
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Osman, Maisarah, and Norhasmah Mohd Zain. "Knowledge and Practices of Cardiovascular Diseases Prevention Among Patients With Type 2 Diabetes Mellitus at Hospital Universiti Sains Malaysia." INTERNATIONAL JOURNAL OF CARE SCHOLARS 4, no. 1 (January 31, 2021): 18–28. http://dx.doi.org/10.31436/ijcs.v4i1.163.
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Background: Type 2 diabetes mellitus (T2DM) is a well-known risk factor for cardiovascular disease (CVD). Healthy lifestyle practices can prevent cardiovascular complications among type 2 diabetes mellitus patients, but most studies showed that many people ignore these preventive measures. This study aimed to evaluate the knowledge and practices of cardiovascular disease prevention among patients with type 2 diabetes mellitus at Hospital Universiti Sains Malaysia (USM). Methods: The study involved 54 respondents through systematic random sampling. The self-administered questionnaire was used for data collection from February 2020 to March 2020. Findings: The mean age of the respondents was 54.50 ± 15.04 years. The findings revealed that 50% of the respondents had good knowledge while the other 50% had poor knowledge. Half (51.90%) of the respondents had poor practice regarding cardiovascular disease prevention. Factors significantly associated with the level of cardiovascular disease prevention practice were ethnic (p<0.05) and monthly household income (p<0.03). Conclusion: The knowledge of cardiovascular disease among the respondents was average but the practices of a healthy lifestyle to prevent the disease were still inadequate. The study emphasizes the need for more effective educational programs about cardiovascular disease and the recommended healthy lifestyle practices precisely for diabetic patients to keep the complication at bay thus achieving a better quality of life.
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Gavrilov, D. V., T. Yu Kuznetsova, M. A. Druzhilov, I. N. Korsakov, and A. V. Gusev. "Predicting the subclinical carotid atherosclerosis in overweight and obese patients using a machine learning model." Russian Journal of Cardiology 27, no. 4 (February 2, 2022): 4871. http://dx.doi.org/10.15829/29/1560-4071-2022-4871.
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Aim. To develop a model for predicting the subclinical carotid atherosclerosis (SCA) in order to refine cardiovascular risk (CVR) using machine learning methods in overweight and obese patients without hypertension, diabetes and/or cardiovascular disease (CVD).Material and methods. Anonymized database (DB) Webiomed (2.9 million patients) was used. There were following inclusion criteria: age ≥18 years, body mass index ≥25 kg/m2, availability of data on ultrasound of extracranial arteries. Patients with hypertension, diabetes and/or CVD were excluded from the analysis. Data on 5750 patients were selected, of which atherosclerotic plaques were detected in 385 people. The final data set contained information on 447 patients, 197 (44,1%) of which had SCA. Quantitative and categorical traits for model training were taken with 40% occupancy in the database. The number of final traits for machine learning was 28. When creating the model, 3 Random Forest algorithms, AdaBoostClassifier, KNeighborsClassifier and the Scikit-learn library were used. To improve the model performance, the fill missing function was used. The target parameters of the model were given a predictive ability (accuracy) of at least 75%, while the area under the ROC curve was at least 0,75.Results. The resulting dataset was divided into training and test parts in a ratio of 80:20. Depending on the applied algorithms, the learned model was characterized by a predictive ability of 75-97%, sensitivity of 77-92%, specificity of 80-98%, and area under the ROC-curve of 0,88-0,97. Taking into account the accuracy metrics, the best results were obtained for the model learned by the Random Forest algorithm (95%, 92%, 98% and 0,95, respectively).Conclusion. The developed model can help a physician make a decision to refer an overweight and obese patient without cardiovascular diseases for ultrasound of extracranial arteries, which contributes to a more accurate CVR stratification. The introduction of such risk stratification algorithms into practice will increase the accuracy and quality of CVR prediction and optimize the system of preventive measures.
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Gavrilov, D. V., T. Yu Kuznetsova, M. A. Druzhilov, I. N. Korsakov, and A. V. Gusev. "Predicting the subclinical carotid atherosclerosis in overweight and obese patients using a machine learning model." Russian Journal of Cardiology 27, no. 4 (February 2, 2022): 4871. http://dx.doi.org/10.15829/1560-4071-2022-4871.
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Aim. To develop a model for predicting the subclinical carotid atherosclerosis (SCA) in order to refine cardiovascular risk (CVR) using machine learning methods in overweight and obese patients without hypertension, diabetes and/or cardiovascular disease (CVD).Material and methods. Anonymized database (DB) Webiomed (2.9 million patients) was used. There were following inclusion criteria: age ≥18 years, body mass index ≥25 kg/m2, availability of data on ultrasound of extracranial arteries. Patients with hypertension, diabetes and/or CVD were excluded from the analysis. Data on 5750 patients were selected, of which atherosclerotic plaques were detected in 385 people. The final data set contained information on 447 patients, 197 (44,1%) of which had SCA. Quantitative and categorical traits for model training were taken with 40% occupancy in the database. The number of final traits for machine learning was 28. When creating the model, 3 Random Forest algorithms, AdaBoostClassifier, KNeighborsClassifier and the Scikit-learn library were used. To improve the model performance, the fill missing function was used. The target parameters of the model were given a predictive ability (accuracy) of at least 75%, while the area under the ROC curve was at least 0,75.Results. The resulting dataset was divided into training and test parts in a ratio of 80:20. Depending on the applied algorithms, the learned model was characterized by a predictive ability of 75-97%, sensitivity of 77-92%, specificity of 80-98%, and area under the ROC-curve of 0,88-0,97. Taking into account the accuracy metrics, the best results were obtained for the model learned by the Random Forest algorithm (95%, 92%, 98% and 0,95, respectively).Conclusion. The developed model can help a physician make a decision to refer an overweight and obese patient without cardiovascular diseases for ultrasound of extracranial arteries, which contributes to a more accurate CVR stratification. The introduction of such risk stratification algorithms into practice will increase the accuracy and quality of CVR prediction and optimize the system of preventive measures.
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Telesca, Vito, Gianfranco Castronuovo, Gianfranco Favia, Cristina Marranchelli, Vito Alberto Pizzulli, and Maria Ragosta. "Effects of Meteo-Climatic Factors on Hospital Admissions for Cardiovascular Diseases in the City of Bari, Southern Italy." Healthcare 11, no. 5 (February 26, 2023): 690. http://dx.doi.org/10.3390/healthcare11050690.
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The objective of this study was to determine the relationship between weather conditions and hospital admissions for cardiovascular diseases (CVD). The analysed data of CVD hospital admissions were part of the database of the Policlinico Giovanni XXIII of Bari (southern Italy) within a reference period of 4 years (2013–2016). CVD hospital admissions have been aggregated with daily meteorological recordings for the reference time interval. The decomposition of the time series allowed us to filter trend components; consequently, the non-linear exposure–response relationship between hospitalizations and meteo-climatic parameters was modelled with the application of a Distributed Lag Non-linear model (DLNM) without smoothing functions. The relevance of each meteorological variable in the simulation process was determined by means of machine learning feature importance technique. The study employed a Random Forest algorithm to identify the most representative features and their respective importance in predicting the phenomenon. As a result of the process, the mean temperature, maximum temperature, apparent temperature, and relative humidity have been determined to be the most suitable meteorological variables as the best variables for the process simulation. The study examined daily admissions to emergency rooms for cardiovascular diseases. Using a predictive analysis of the time series, an increase in the relative risk associated with colder temperatures was found between 8.3 °C and 10.3 °C. This increase occurred instantly and significantly 0–1 days after the event. The increase in hospitalizations for CVD has been shown to be correlated to high temperatures above 28.6 °C for lag day 5.
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Khadka, M. "Knowledge Regarding Modifiable Risk Factors of Coronary Atherosclerosis Heart Diseases in Kathmandu Municipality." Nepalese Heart Journal 9, no. 1 (July 21, 2013): 37–42. http://dx.doi.org/10.3126/njh.v9i1.8347.
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Background Progressive urbanization and adoption of the “western” lifestyle contributes to the rising burden of cardiovascular disease in the developing world Coronary Atherosclerotic Heart Disease is no longer confined by geographical area or socioeconomic boundary. The prevalence of Coronary Atherosclerotic Heart Disease is increasing in Nepal. Knowledge is an important pre-requisite for implementing both primary and secondary preventive strategies for cardiovascular diseases. This investigation attempts to quantify knowledge of modifiable risk factors of Coronary Atherosclerotic Heart Disease among sample population in Kathmandu metropolitan city. Methods Community based cross-sectional descriptive study design using quantitative method of study was conducted in ward no 5 of Kathmandu out of 35 wards. Selection was done by simple random technique (lottery method). Total house hold serial number of selected ward was identified from election commission record section and data was collected using systemic random sampling. The household head aged 18 years and above was taken as representative sample (n= 196). Standard questionnaire was used to interview participants. The risk factors specifically included smoking, hypertension, elevated cholesterol levels, diabetes mellitus and obesity. Results The mean age (SD) of the 196 participants was 51.26 (13.56) years. Of the participants only 22% had good level of knowledge regarding modifiable risk factors of Coronary Atherosclerotic Heart Disease. This study showed that majority of the respondent lack predefined good level of knowledge regarding modifiable risk factors of Coronary Atherosclerotic Heart Disease. 85.2%, 61.73%, 40.31%, 28.6%, 17.86% correctly identified hypertension, obesity, cholesterol, smoking and diabetes mellitus respectively as modifiable risk factor of Coronary Atherosclerotic Heart Disease. Stud y found association of good level of knowledge in male participants (p=0.006), Brahmin cast (p=0.001), living in nuclear family (p= 0.041), ex-smoker (p=0.06), doing regular exercise ( p= 0.006). Conclusion This study call for efforts such as targeted public health education to increase the level of knowledge about the modifiable risk factors of heart disease. DOI: http://dx.doi.org/10.3126/njh.v9i1.8347 Nepalese Heart Journal Vol.9(1) 2012 pp.37-42
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Hasan, Ruby. "Comparative Analysis of Machine Learning Algorithms for Heart Disease Prediction." ITM Web of Conferences 40 (2021): 03007. http://dx.doi.org/10.1051/itmconf/20214003007.
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In the last few years, cardiovascular diseases have emerged as one of the most common causes of deaths worldwide. The lifestyle changes, eating habits, working cultures etc, has significantly contributed to this alarming issue across the globe including the developed, underdeveloped and developing nations. Early detection of the initial signs of cardiovascular diseases and the continuous medical supervision can help in reducing rising number of patients and eventually the mortality rate. However with limited medical facilities and specialist doctors, it is difficult to continuously monitor the patients and provide consultations. Technological interventions are required to facilitate the patient monitoring and treatment. The healthcare data generated through various medical procedures and continuous patient monitoring can be utilized to develop efficient prediction models for cardiovascular diseases. The early prognosis of cardiovascular illnesses can aid in making decisions on life-style changes in high hazard sufferers and in turn lessen the complications, which may be an outstanding milestone inside the field of medicine. This paper studies some of the most widely used machine learning algorithms for heart disease prediction by using the medical data and historical information. The various techniques are discussed and a comparative analysis of the same is presented. This report compares five common strategies for predicting the chance of heart attack that have been published in the literature. KNN, Decision Tree, Gaussian Naive Bayes, Logistic Regression, and Random Forest are some of the approaches used. Further, the paper also highlights the advantages and disadvantages of using the various techniques for developing the prediction models.
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Liu, Jimin, Xueyu Dong, Huiqi Zhao, and Yinhua Tian. "Predictive Classifier for Cardiovascular Disease Based on Stacking Model Fusion." Processes 10, no. 4 (April 13, 2022): 749. http://dx.doi.org/10.3390/pr10040749.
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The etiology of cardiovascular disease is still an unsolved world problem, and high morbidity, disability, and mortality are the main characteristics of cardiovascular diseases. There is, therefore, a need for effective and rapid early prediction of likely outcomes in patients with cardiovascular disease using artificial intelligence (AI) techniques. The Internet of Things (IoT) is becoming a catalyst for enhancing the capabilities of AI applications. Data are collected through IoT sensors and analyzed and predicted using machine learning (ML). Existing traditional ML models do not handle data inequities well and have relatively low model prediction accuracy. To address this problem, considering the data observation mechanism and training methods of different algorithms, this paper proposes an ensemble framework based on stacking model fusion, from Support Vector Machines (SVM), K-Nearest Neighbor (KNN), Logistic Regression (LR), Random Forest (RF), Extra Tree (ET), Gradient Boosting Decision Tree (GBDT), XGBoost, LightGBM, CatBoost, and Multilayer Perceptron (MLP) (10 classifiers to select the optimal base learners). In order to avoid the overfitting phenomenon generated by the base learners, we use the Logistic Regression (LR) simple linear classifier as the meta learner. We validated the proposed algorithm using a fused Heart Dataset from several UCI machine learning repositories and another publicly available Heart Attack Dataset, and compared it with 10 single classifier models. The experimental results show that the proposed stacking classifier outperforms other classifiers in terms of accuracy and applicability.