This study examines the role of feature selection methods in optimizing machine learning algorithms for predicting heart disease. The Cleveland Heart disease dataset with sixteen feature selection techniques in three categories of filter, wrapper, and evolutionary were used. Seven algorithms were applied to identify the best models for heart disease prediction. The results demonstrate that feature selection resulted in significant improvements in model performance in some methods, while it led to a decrease in model performance in other models. SVM-based filtering methods have a best-fit accuracy of 85.5. Filter feature selection methods with the highest number of features selected outperformed other methods in terms of models’ ACC, Precision, and F-measures. Wrapper-based and evolutionary algorithms improved models’ performance from sensitivity and specificity points of view.
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