A systematic review of machine learning in heart disease prediction

Short summary

Machine learning models, particularly ensemble methods on structured data and deep learning on unstructured data (ECG, imaging), show high accuracy in heart disease prediction, but a significant gap hinders clinical translation due to lack of external validation, reliance on limited datasets, and model interpretability issues.

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