CORONARY ARTERY DISEASE PREDICTION SYTSEM USING MACHINE LEARNING

ABSTRACT The application of machine learning (ML) algorithms in the early detection of Coronary Artery Disease (CAD) has emerged as a promising approach to improve diagnostic accuracy and patient outcomes. This study investigates various ML techniques, including Support Vector Machines (SVM), Artificial Neural Networks (ANN), and Random Forests, to predict the presence of CAD based on clinical data. Utilizing extensive datasets that encompass multiple risk factors associated with CAD, the models were trained and validated to assess their predictive capabilities. Results indicate that ML algorithms can achieve high accuracy rates, with SVM demonstrating an accuracy of 89.73% and ANN models exceeding 93%. These findings suggest that machine learning can significantly enhance the diagnostic process by providing timely insights for clinicians, enabling early intervention and management of CAD. The study underscores the potential of integrating machine learning into clinical practice to facilitate proactive healthcare strategies for patients at risk of coronary artery disease. Keywords: Coronary Artery Disease (CAD), Machine Learning, Predictive Modeling, Support Vector Machines (SVM), Artificial Neural Networks (ANN), Random Forests, Diagnostic Accuracy, Risk Factors, Clinical Data, Early Detection.