This study examines the use of machine learning (ML) models for the early detection of cardiovascular diseases (CVDs). Early diagnosis is critical for effective treatment, yet traditional methods may be limited in speed and accuracy. ML algorithms, including logistic regression, support vector machines, and neural networks, analyze patient data such as medical history, lab results, and imaging to identify risk patterns. These models enable personalized risk assessment, improve clinical decision-making, and support timely intervention. The study also addresses challenges such as data privacy, model interpretability, and integration into healthcare systems. Overall, ML provides a promising approach to enhance early detection and management of cardiovascular diseases.

machine learning, cardiovascular diseases, early detection, predictive models, risk assessment, healthcare analytics, supervised learning, neural networks.

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