The study aimed to predict heart disease using machine learning and feature selection techniques. They used data from the Cleveland Clinic Heart Disease dataset and tested different methods to identify important features. By combining Random Forest with Recursive Feature Elimination, they achieved the highest accuracy, precision, recall, and F-score. This model also had the best discriminatory ability with an AUC Score of 92%. This research can help improve heart disease diagnosis and create predictive models for early detection.