Early prediction of cardiovascular disease using machine learning: Unveiling risk factors from health records

Abstract

This article focuses on the early prediction of cardiovascular disease (CVD) through the application of machine learning to health records. This study systematically reviews existing literature and employs advanced machine learning algorithms to discern predictive factors within electronic health data. Key findings highlight the significance of genetic predispositions, lifestyle choices, and clinical markers as influential contributors to CVD development. The integration of these factors into machine learning models demonstrates notable accuracy in preemptive risk assessment. The implications of this research are profound, offering potential advancements in preventive healthcare strategies, personalized interventions, and resource allocation for populations at heightened cardiovascular risk.