Cardiovascular diseases (CVD) are among the leading causes of morbidity and mortality
worldwide. Early detection and risk assessment of CVD are critical for preventing adverse
health outcomes. Recently, artificial intelligence (AI) and machine learning (ML) have
emerged as transformative technologies in healthcare, particularly in the prediction of
cardiovascular disease risk. These technologies leverage large datasets and complex
algorithms to improve diagnostic accuracy, predict future cardiovascular events, and
personalize prevention strategies. This paper examines the role of AI and ML in predicting
CVD risk, discusses various methodologies used, evaluates their effectiveness, and explores
the challenges and future prospects of these technologies in clinical practice.
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