{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T01:08:04Z","timestamp":1784164084498,"version":"3.55.0"},"reference-count":240,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,2,13]],"date-time":"2024-02-13T00:00:00Z","timestamp":1707782400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Cardiovascular disease is the leading cause of global mortality and responsible for millions of deaths annually. The mortality rate and overall consequences of cardiac disease can be reduced with early disease detection. However, conventional diagnostic methods encounter various challenges, including delayed treatment and misdiagnoses, which can impede the course of treatment and raise healthcare costs. The application of artificial intelligence (AI) techniques, especially machine learning (ML) algorithms, offers a promising pathway to address these challenges. This paper emphasizes the central role of machine learning in cardiac health and focuses on precise cardiovascular disease prediction. In particular, this paper is driven by the urgent need to fully utilize the potential of machine learning to enhance cardiovascular disease prediction. In light of the continued progress in machine learning and the growing public health implications of cardiovascular disease, this paper aims to offer a comprehensive analysis of the topic. This review paper encompasses a wide range of topics, including the types of cardiovascular disease, the significance of machine learning, feature selection, the evaluation of machine learning models, data collection &amp; preprocessing, evaluation metrics for cardiovascular disease prediction, and the recent trends &amp; suggestion for future works. In addition, this paper offers a holistic view of machine learning\u2019s role in cardiovascular disease prediction and public health. We believe that our comprehensive review will contribute significantly to the existing body of knowledge in this essential area.<\/jats:p>","DOI":"10.3390\/a17020078","type":"journal-article","created":{"date-parts":[[2024,2,14]],"date-time":"2024-02-14T04:18:22Z","timestamp":1707884302000},"page":"78","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":87,"title":["A Review of Machine Learning\u2019s Role in Cardiovascular Disease Prediction: Recent Advances and Future Challenges"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5686-2759","authenticated-orcid":false,"given":"Marwah Abdulrazzaq","family":"Naser","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, University of Baghdad, Al-Jadriya, Baghdad 10071, Iraq"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aso Ahmed","family":"Majeed","sequence":"additional","affiliation":[{"name":"Department of Basic Science, University of Kirkuk, Kirkuk 36001, Iraq"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7937-3093","authenticated-orcid":false,"given":"Muntadher","family":"Alsabah","sequence":"additional","affiliation":[{"name":"Medical Technical College, Al-Farahidi University, Baghdad 10071, Iraq"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3913-9769","authenticated-orcid":false,"given":"Taha Raad","family":"Al-Shaikhli","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering Techniques, Al-Nisour University College, Baghdad 10071, Iraq"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6338-5943","authenticated-orcid":false,"given":"Kawa M.","family":"Kaky","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering Techniques, Al-Nisour University College, Baghdad 10071, Iraq"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"721","DOI":"10.1016\/S0140-6736(14)62254-6","article-title":"Transitioning health systems for multimorbidity","volume":"386","author":"Atun","year":"2015","journal-title":"Lancet"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"020303","DOI":"10.7189\/jogh.08.020303","article-title":"Artificial intelligence, machine learning and health systems","volume":"8","author":"Panch","year":"2018","journal-title":"J. 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