A hybrid feature selection and ensemble learning model for heart disease diagnosis
Heart disease remains one of the leading causes of mortality worldwide, making early and accurate diagnosis essential for improving patient outcomes. This study proposes a hybrid feature selection and ensemble learning framework for heart disease diagnosis using a publicly available dataset containing 303 records and 13 clinical attributes. The preprocessing pipeline included missing-value imputation, one-hot encoding of categorical variables, and minimum–maximum normalization. Support vector machine–recursive feature elimination (SVM–RFE) was used to identify the most informative clinical features and remove redundant attributes. Multiple machine learning classifiers, including logistic regression, k-nearest neighbors, SVMs, decision trees, random forests, and extreme gradient boosting (XGBoost), were evaluated. Bayesian optimization was applied to tune model hyperparameters, and performance was assessed using an 80:20 train–test split together with stratified five-fold cross-validation. The proposed ensemble model achieved 93.62% accuracy, 94.08% precision, 93.11% recall, 93.58% F1-score, and 0.96 receiver operating characteristic area under the curve. The selected features included chest pain type, ST-slope, exercise-induced angina, maximum heart rate, and age, all of which are clinically meaningful predictors of cardiovascular risk. The results show that combining SVM-RFE, hyperparameter optimization, and ensemble learning improved predictive performance, generalization, and interpretability. The proposed framework can support early heart disease screening and clinical decision-making.