Cardiovascular Risk Prediction Using Machine Learning: Advances and Clinical Translation

Abstract

Cardiovascular disease (CVD) remains the leading cause of global morbidity and mortality, underscoring the need for accurate and timely risk prediction to guide prevention and clinical decision-making. Traditional risk assessment models, while widely adopted, are often limited by linear assumptions, restricted variable sets, and suboptimal performance across diverse populations. In recent years, machine learning (ML) techniques have emerged as powerful tools to enhance cardiovascular risk prediction by leveraging high-dimensional data and capturing complex, non-linear relationships among risk factors. This review synthesizes recent advances in ML-based cardiovascular risk prediction, including supervised learning approaches such as random forests, support vector machines, gradient boosting, and deep learning architectures. We examine the integration of heterogeneous data sources electronic health records, imaging, genomics, and wearable device data and their contributions to improved predictive accuracy and personalized risk stratification. Comparative analyses with conventional models, such as the Framingham Risk Score and pooled cohort equations, are discussed to highlight performance gains and limitations. Furthermore, we evaluate key challenges hindering clinical translation, including issues of model interpretability, data quality and bias, generalizability across populations, and regulatory and ethical considerations. Strategies to enhance trust and adoption such as explainable AI methods, external validation, and prospective clinical trials are also explored. Finally, we outline future directions for integrating ML models into clinical workflows, emphasizing the importance of interdisciplinary collaboration and robust validation frameworks. Overall, ML-driven cardiovascular risk prediction holds significant promise for advancing precision medicine, but its successful implementation in routine clinical practice requires careful consideration of methodological rigor, transparency, and real-world applicability.