Advances in Machine Learning-Driven Heart Disease Prediction: A Systematic Review
Abstract
Heart disease remains a major global health concern and one of the leading causes of mortality worldwide. The growing availability of clinical datasets, electronic health records, and biomedical signals has led to increased adoption of machine learning (ML) and deep learning (DL) methods for early diagnosis and risk prediction. This review synthesizes recent advancements in ML-based heart disease prediction, drawing on contemporary studies that examine classical ML algorithms, hybrid approaches, deep architectures, and explainable AI techniques. The review outlines commonly employed data sources, preprocessing strategies, and evaluation metrics, and highlights the strengths and limitations of state-of-the-art models. A detailed research gap analysis identifies constraints such as limited generalizability, lack of multi-modal integration, data imbalance, and insufficient clinical validation. The paper concludes with future research directions, including federated learning, IoT-enabled real-time monitoring, quantum-enhanced models, and clinically interpretable AI systems. Overall, this review provides consolidated insights into the evolving role of machine learning in supporting early and accurate heart disease prediction.
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