A comparative performance analysis of ensemble learning and regularized neural networks in cardiovascular risk prediction.

INTRODUCTION: Heart disease classification using small structured clinical datasets requires evaluation procedures that account for sampling variability, model-selection bias, probability calibration, and clinically relevant operating characteristics. This study presents an internally validated comparison of classical classifiers, modern tree ensembles, and regularized artificial neural networks using a cleaned Cleveland heart disease dataset containing 303 observations, 13 predictors, and a binary outcome. METHODS: Continuous variables were standardized, nominal variables were one-hot encoded, and all preprocessing transformations were fitted exclusively within training folds. Model performance was evaluated using five-fold outer cross-validation repeated five times, with inner stratified cross-validation for hyperparameter optimisation. Twelve models were assessed using accuracy, sensitivity, specificity, positive and negative predictive values, F1-score, Matthew's correlation coefficient, ROC-AUC, PR-AUC, Brier score, and calibration measures. RESULTS: CatBoost achieved the highest mean ROC-AUC of 0.909 ± 0.041 and F1-score of 0.862 ± 0.041, while Logistic Regression achieved a comparable ROC-AUC of 0.906 ± 0.041. Random Forest obtained the highest mean PR-AUC of 0.916 ± 0.040. CatBoost did not significantly outperform Logistic Regression in ROC-AUC, PR-AUC, F1-score, or paired classification errors. Held-out permutation analysis identified the number of major vessels, thalassemia status, and chest-pain category as the most influential predictors. DISCUSSION: The findings indicate that no model family was universally superior and that a carefully regularized linear model remained competitive with substantially more complex approaches. These findings represent internal validation only and require confirmation using independent clinical cohorts before deployment.