Key result
Hybrid deep learning models yield up to ~83% accuracy for predicting heart disease without significant advantages.
Hybrid deep learning models for heart disease prediction show competitive performance but require longer training times and do not significantly outperform classical machine learning models like Random Forest or SVM.
Hybrid models show competitive performance on UCI data; leaves open prospective validation before any clinical use.
This study presents a systematic evaluation of classical machine learning, deep learning, and hybrid models for heart disease prediction using the University of California, Irvine dataset. A standardized framework with consistent preprocessing and 5-fold cross-validation was applied to ensure fair comparison. The results indicate that the hybrid models achieved competitive performance across multiple metrics, with the proposed convolutional neural network (CNN)–Attention–eXtreme Gradient Boosting (XGBoost) ensemble obtaining an accuracy of 82.07% ± 2.68% and an F1 score of 81.78% ± 2.68%. However, CNN + Support Vector Machine (SVM) achieved the highest accuracy (83.37% ± 2.11%) among all models, whereas Random Forest achieved the highest area under the curve (89.36% ± 1.42%) and showed strong calibration performance. In terms of probabilistic calibration, Brier Skill Score (BSS) analysis showed that all models outperformed the baseline (BSS > 0), with CNN + SVM (0.477), SVM (0.474), and Random Forest (0.465) achieving the highest calibration skill. Statistical analysis using the Friedman test and post hoc Wilcoxon signed-rank tests with Bonferroni correction revealed no statistically significant pairwise differences among the models, suggesting that the observed performance differences were marginal despite numerical variations. Computational analysis highlighted a trade-off between performance and efficiency, as hybrid models required longer training times (up to 7.59 s per fold) than lightweight tree-based models (<0.3 s). A cross-country evaluation further revealed reduced generalizability under domain shift.
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Buaphan et al. (2026) studied heart disease. Hybrid deep learning models vs. Classical machine learning models was evaluated on Accuracy for heart disease prediction. Hybrid deep learning models such as CNN + SVM achieved up to 83.37% accuracy for heart disease prediction, though performance differences among models were not statistically significant.
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