[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119898-en":3,"doc-seo-119898-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119898,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Machine Learning-Based Predictive Models for Detection of Cardiovascular Diseases - Article","Cardiovascular diseases pose a major global health challenge, requiring reliable and more effective detection methods. Existing predictive approaches still face gaps, including insufficient handling of imbalanced datasets that can bias outcomes, particularly for minority classes. This study focuses on early heart-disease detection—especially myocardial infarction—using machine learning techniques and a literature-driven review to address imbalance. Seven classifiers, including K-Nearest Neighbors, SVM, Logistic Regression, CNN, Gradient Boost, XGBoost, and Random Forest, were evaluated, highlighting fine-tuned XGBoost with 98.50% accuracy, 99.14% precision, 98.29% recall, and 98.71% F1.","diagnostics  \nArticle  \nMachine Learning-Based Predictive Models for Detection of Cardiovascular Diseases  \nAdedayo Ogunpola 1, Faisal Saeed 1, *, Shadi Basurra 1, Abdullah M. Albarrak 2 and Sultan Noman Qasem 2  \nCitation: Ogunpola, A.; Saeed, F.; Basurra, S.; Albarrak, A.M.; Qasem, S.N. Machine Learning-Based Predictive Models for Detection of Cardiovascular Diseases. Diagnostics 2024, 14, 144. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/diagnostics14020144](10.3390/diagnostics14020144)  \nAcademic Editor: Mugahed A. Al-antari  \nReceived: 27 November 2023  \nRevised: 21 December 2023  \nAccepted: 25 December 2023  \nPublished: 8 January 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 DAAI Research Group, College of Computing and Digital Technology, Birmingham City University,  \nBirmingham B4 7XG, UK; [adedayo.ogunpola@mail.bcu.ac.uk](adedayo.ogunpola@mail.bcu.ac.uk) (A.O.); [shadi.basurra@bcu.ac.uk](shadi.basurra@bcu.ac.uk) (S.B.)  \n2 Computer Science Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia; [amsbarrak@imamu.edu.sa](amsbarrak@imamu.edu.sa) (A.M.A.); [snmohammed@imamu.edu.sa](snmohammed@imamu.edu.sa) (S.N.Q.)  \n* Correspondence: [faisal.saeed@bcu.ac.uk](faisal.saeed@bcu.ac.uk)  \nAbstract: Cardiovascular diseases present a significant global health challenge that emphasizes the critical need for developing accurate and more effective detection methods. Several studies have contributed valuable insights in this field, but it is still necessary to advance the predictive models and address the gaps in the existing detection approaches. For instance, some of the previous studies have not considered the challenge of imbalanced datasets, which can lead to biased predictions, especially when the datasets include minority classes. This study’s primary focus is the early detection of heart diseases, particularly myocardial infarction, using machine learning techniques. It tackles the challenge of imbalanced datasets by conducting a comprehensive literature review to identify effective strategies. Seven machine learning and deep learning classifiers, including K-Nearest Neighbors, Support Vector Machine, Logistic Regression, Convolutional Neural Network, Gradient Boost, XGBoost, and Random Forest, were deployed to enhance the accuracy of heart disease predictions. The research explores different classifiers and their performance, providing valuable insights for developing robust prediction models for myocardial infarction. The study’s outcomes emphasize the effectiveness of meticulously fine-tuning an XGBoost model for cardiovascular diseases. This optimization yields remarkable results: 98.50% accuracy, 99.14% precision, 98.29% recall, anda 98.71% F1 score. Such optimization significantly enhances the model’s diagnostic accuracy for heart disease.  \nKeywords: cardiovascular diseases; deep learning; disease detection; heart diseases; machine learning; ensemble learning; XGBoost  \n1. Introduction  \nThe heart plays a crucial role in sustaining life by effectively pumping oxygenated blood and regulating important hormones to maintain optimal blood pressure levels. Any deviation from its functioning can lead to the development of heart conditions, collectively known as cardiovascular diseases (CVD) . CVD includes a range of disorders that affect both the heart and blood vessels, such as cerebrovascular problems, congenital anomalies, pulmonary embolisms, irregular heart rhythms (arrhythmias), peripheral arterial issues, coronary artery disease (CAD), rheumatic heart ailments, coronary heart disease (CHD), and card","cbCain78UC1S3wus","https://ap.wps.com/l/cbCain78UC1S3wus","pdf",1977358,1,19,"English","en",105,"# Abstract\n# Keywords\n# 1. Introduction","[{\"question\":\"What problem does the study address in cardiovascular disease detection?\",\"answer\":\"The study targets early detection of heart diseases, especially myocardial infarction, and focuses on improving predictive performance under imbalanced datasets that can cause biased predictions.\"},{\"question\":\"Which machine learning models were evaluated for detection?\",\"answer\":\"Seven classifiers were tested: K-Nearest Neighbors, Support Vector Machine, Logistic Regression, Convolutional Neural Network, Gradient Boost, XGBoost, and Random Forest.\"},{\"question\":\"Why is XGBoost emphasized, and what results were achieved?\",\"answer\":\"The research shows that carefully fine-tuning XGBoost improves cardiovascular diagnostic accuracy, reaching 98.50% accuracy, 99.14% precision, 98.29% recall, and 98.71% F1 score.\"}]","Machine Learning-Based Predictive Models for Detection of Cardiovascular Diseases - Article | 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problem does the study address in cardiovascular disease detection?","Question",{"text":76,"@type":77},"The study targets early detection of heart diseases, especially myocardial infarction, and focuses on improving predictive performance under imbalanced datasets that can cause biased predictions.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which machine learning models were evaluated for detection?",{"text":81,"@type":77},"Seven classifiers were tested: K-Nearest Neighbors, Support Vector Machine, Logistic Regression, Convolutional Neural Network, Gradient Boost, XGBoost, and Random Forest.",{"name":83,"@type":74,"acceptedAnswer":84},"Why is XGBoost emphasized, and what results were achieved?",{"text":85,"@type":77},"The research shows that carefully fine-tuning XGBoost improves cardiovascular diagnostic accuracy, reaching 98.50% accuracy, 99.14% precision, 98.29% recall, and 98.71% F1 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