Supervised machine learning for cardiovascular risk prediction and clinical event forecasting: a systematic review of multi-modal models, cross-validation strategies, and fairness-aware approaches

Background and Objectives: Cardiovascular disease (CVD) remains the leading global cause of mortality, accounting for approximately 17.9 million deaths annually. Although conventional cardiovascular risk prediction tools such as the Framingham Risk Score, SCORE2, and Pooled Cohort Equations (PCE) are widely used, supervised machine learning (SML) approaches have shown considerable potential to improve predictive accuracy and clinical decision-making. Despite the rapid advancement of gradient boosting, ensemble learning, and fairness-aware machine learning techniques, a comprehensive systematic review evaluating SML models for cardiovascular risk prediction, including their predictive performance, validation strategies, multi-modal data integration, and demographic fairness, has been lacking. Methods: A systematic search of PubMed/MEDLINE, Embase, IEEE Xplore, Web of Science, and the ACM Digital Library was conducted for studies published between January 2017 and January 2025, following PRISMA 2020 guidelines. The review protocol was registered in PROSPERO (CRD42025421673). Eligible studies included those developing or externally validating SML models for cardiovascular risk predict