A Comprehensive Review on Heart Disease Risk Prediction using Machine Learning and Deep Learning Algorithms

• Published in: Archives of computational methods in engineering Vol. 32; no. 3; pp. 1763 - 1795
• Main Authors: Karna, Vishnu Vardhana Reddy, Karna, Viswavardhan Reddy, Janamala, Varaprasad, Devana, V. N. Koteswara Rao, Ch, V. Ravi Sankar, Tummala, Aravinda Babu
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.04.2025; Springer Nature B.V
• Subjects: Alcohol; Algorithms; Atherosclerosis; Blood pressure; Brain research; Cardiology; Cardiovascular disease; Coronary vessels; Data mining; Deep learning; Effectiveness; Electrocardiography; Engineering; Ensemble learning; Exercise; Fatalities; Health care industry; Heart attacks; Heart diseases; Heart failure; Hyperglycemia; Machine learning; Mathematical and Computational Engineering; Medical diagnosis; Medical prognosis; Medical records; Mortality; Patients; Review; Risk factors; Stroke; Vein & artery diseases; United States > US
• ISSN: 1134-3060; 1886-1784
• DOI: 10.1007/s11831-024-10194-4

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 electrocardiograms—commonly used in clinics and hospitals to detect abnormal heart rhythms—are 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.