ML-Driven Model for Predicting Heart Disease Risk: A Performance Evaluation of SVM and Random Forest Models

Abstract

The utilization of machine learning algorithms in the diagnosis and treatment of medical diseases has grown more common, especially for heart disease prediction. The increasing incidence of abrupt cardiac deaths necessitates precise prognosis and diagnosis. Data mining techniques and machine learning algorithms significantly contribute to this field by facilitating the creation of software that aids healthcare practitioners in making informed judgments regarding heart disease risk and diagnosis.