Machine Learning for Cardiovascular Risk Prediction: A Practical Primer for Clinicians

Machine learning (ML) offers powerful tools for clinical risk prediction, enhancing traditional statistical approaches through improved pattern recognition and predictive capability-its role and use in cardiovascular care and research is expanding. This guide provides readers with foundational knowledge of ML methodology for risk prediction, including model development, validation, and implementation considerations. We discuss supervised and unsupervised learning approaches, feature selection, performance metrics, and advanced techniques such as deep learning and interrupted time-series analysis. Challenges regarding interpretability, bias, and clinical integration are addressed alongside practical recommendations for readers evaluating or implementing ML-based risk prediction tools in practice. This primer equips readers with essential knowledge to critically appraise ML-based risk prediction models and collaborate effectively with data scientists.