<![CDATA[Heart disease is one of the leading causes of mortality worldwide, and accurate prediction models are essential to support early diagnosis. However, conventional Random Forest classifiers generally utilize all available features, although not all features contribute equally to classification performance, resulting in unnecessary model complexity. This study proposes an Improved Genetic Algorithm (IGA) that extends the conventional Genetic Algorithm through elitism, adaptive crossover, and adaptive mutation operators to optimize feature selection for Random Forest-based heart disease classification. The proposed method was evaluated using the Cardiovascular Disease Dataset from Kaggle, which consisting of 1,000 records and 14 variables, where 12 predictor features were used for model development. The experimental procedure included data preprocessing, train-test splitting, class imbalance handling using SMOTE on the training set, feature normalization, Random Forest modeling, feature selection using the proposed IGA, and model evaluation. The proposed IGA selected six important features slope, chestpain, restingBP, restingelectro, oldpeak, and gender. The optimized Random Forest model achieved an accuracy of 99.50%, precision of 99.15%, recall of 100.00%, F1-score of 99.57%, and AUC-ROC of 99.90%. These findings indicate that feature selection can simplify the model without compromising classification performance, making the Random Forest + IGA approach a viable alternative for developing more efficient heart disease prediction models.]]>
