Review – Machine learning approaches for cardiovascular disease prediction

Siming Wan, Feng Wan, Xi-jian Dai

Cardiovascular disease is a leading cause of death worldwide and is associated with significant morbidity and mortality. The use of artificial intelligence techniques, particularly machine learning algorithms, has emerged as a transformative approach for enhancing early diagnostic accuracy of disease compared with conventional diagnostic methods. This systematic review examines three core aspects: (1) comparative analysis of current machine learning algorithms in early diagnosis of cardiovascular disease, (2) operational frameworks for clinical implementation, and (3) critical evaluation of regulatory compliance and ethical implications. It summarizes recent advancements in machine learning-based heart disease prediction, outlines a typical workflow for applying machine learning in clinical settings, and discusses the regulatory and ethical challenges associated with its implementation. Finally, this review explores potential directions for future research in this rapidly evolving field.

• Machine learning
• Cardiovascular disease
• Classification
• Prediction
• Feature selection

• Background
• Comparison of ML and conventional methods in CVD prediction
• Innovations in heart disease prediction using ML
• Data processing and evaluation metrics
• Current challenges and prospective directions for future research
• Conclusions

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