Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Reviews Review Faculty Publications- Reviews Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource A Comprehensive Review on Heart Disease Risk Prediction using Machine Learning and Deep Learning Algorithms Description An account of the resource Cardiovascular diseases claim approximately 17.9 million lives annually, with heart attacks and strokes accounting for over 80% of these deaths. Key risk factors, including hypertension, hyperglycemia, dyslipidemia, and obesity, are identifiable, offering opportunities for timely intervention and reduced mortality. Early detection of heart disease enables individuals to adopt lifestyle changes or seek medical treatment. However, conventional diagnostic methods, such as electrocardiogramscommonly used in clinics and hospitals to detect abnormal heart rhythmsare not effective in identifying actual heart attacks. Additionally, angiography, while more precise, is an invasive method, financial strain on patients, and high chances of incorrect diagnosis, highlighting the need for alternative approaches. The main goal of this study was to assess the accuracy of machine learning techniques, including both individual and combined classifiers, in early detection of heart diseases. Furthermore, the study aims to highlight areas where additional research is necessary. Our investigation covers a decade period from 2014 to 2024, including a thorough review of pertinent literature from international conferences and top journals from the databases like Springer, ScienceDirect, IEEEXplore, Web of Science, PubMed, MDPI, Hindawi and so on. The following keywords were used to search the articles: heart disease risk, heart disease prediction, data mining, data preprocessing, machine learning algorithms, ensemble classifiers, deep learning algorithms, feature selection, hyperparameter optimization techniques. We examine the methodologies used and evaluate their effectiveness in predicting cardiovascular conditions. Our findings reveal notable progress in applying machine learning and deep learning in cardiology. The study concludes by proposing a framework that incorporates current machine learning techniques to enhance heart disease prediction. The Author(s) under exclusive licence to International Center for Numerical Methods in Engineering (CIMNE) 2024. Creator An entity primarily responsible for making the resource Karna V.V.R.; Karna V.R.; Janamala V.; Devana V.N.K.R.; Ch V.R.S.; Tummala A.B. Source A related resource from which the described resource is derived Archives of Computational Methods in Engineering Publisher An entity responsible for making the resource available Springer Science and Business Media B.V. Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/s11831-024-10194-4" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s11831-024-10194-4</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85207264313&amp;doi=10.1007%2Fs11831-024-10194-4&amp;partnerID=40&amp;md5=97deb939e860f00bbeb2adcb3f36a3ca" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85207264313&amp;doi=10.1007%2fs11831-024-10194-4&amp;partnerID=40&amp;md5=97deb939e860f00bbeb2adcb3f36a3ca</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 11343060 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Review Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Karna V.V.R., Department of Electronics and Communication Engineering, Aditya University, Surampalem, 533437, India; Karna V.R., Department of Artificial Intelligence and Machine Learning, R V College of Engineering, Bengaluru, 560059, India; Janamala V., Department of Electrical and Electronics Engineering, Christ University, Karnataka, Bengaluru, 560074, India; Devana V.N.K.R., Department of Electronics and Communication Engineering, Aditya University, Surampalem, 533437, India; Ch V.R.S., Department of Electronics and Communication Engineering, Aditya University, Surampalem, 533437, India; Tummala A.B., Department of Electronics and Communication Engineering, Chaitanya Bharati Institute of Technology, Hyderabad, 500075, India