Heart Disease Detection with Machine Learning Algorithms

Heart disease remains a leading cause of morbidity and mortality worldwide, highlighting the need for accurate and scalable predictive models to support early diagnosis and clinical decision-making. In this study, we investigate the effectiveness of machine learning approaches for heart disease prediction using a large, publicly available clinical dataset obtained from IEEE DataPort. The dataset integrates multiple established heart disease cohorts and includes key demographic and clinical variables relevant to cardiovascular risk assessment. Two supervised machine learning models—Random Forest and Support Vector Machine (SVM)—were developed and evaluated to classify the presence of heart disease. Model performance was assessed using accuracy, precision, and recall metrics. The Random Forest model achieved an accuracy of 0.89, with a precision of 0.85 and a recall of 0.83, demonstrating strong overall performance and balanced predictive capability. The SVM model achieved an accuracy of 0.88, with a recall of 0.85 and a precision of 0.81, indicating robust sensitivity in identifying positive cases.