Multi-View Autoencoder Framework with Feature Recalibration and Ensemble Learning for Predicting Heart Disease

Heart disease continues to pose a major challenge to global health, underscoring the need for early, accurate prediction models. In this study, we introduce a new hybrid intelligent framework designed to significantly improve heart disease classification. Our approach combines multi-view deep feature extraction, adaptive feature recalibration, and dynamic ensemble learning to deliver more reliable predictions.The process begins with a multi-view autoencoder that separately captures latent features from demographic, clinical, and diagnostic data. This separation preserves the unique information each data type offers, leading to richer and more meaningful feature representations. Next, we apply a self-adaptive recalibration mechanism that assigns importance weights to each feature based on the data itself. This ensures that features with stronger clinical relevance play a greater role in the model’s decision-making. Finally, we integrate a confidence-aware ensemble of three powerful classifiers—Extra Trees, Random Forest, and XGBoost. This ensemble dynamically adjusts the influence of each model depending on how confident they are at the instance level.We tested the proposed framework across five well-known heart disease datasets, using 10-fold cross-validation to ensure robustness. The results are promising: the model achieved an accuracy of 92.45%, sensitivity of 93.2%, specificity of 91.4%, and an F1-score of 91.4%. It consistently outperformed traditional machine learning methods, recent hybrid ensembles, and even state-of-the-art deep learning models like TabNet, SAINT, NODE, and TabTransformer. Statistical significance was confirmed via Friedman and Wilcoxon signed-rank tests (p < 0.01). To support interpretability, we used SHAP analysis, which highlighted key medical predictors such as chest pain type, number of major vessels, and ST depression.In summary, our results demonstrate that combining multi-view representation learning with adaptive recalibration and dynamic ensemble strategies leads to a highly effective, interpretable, and clinically relevant tool for early heart disease prediction. This framework holds strong promise for integration into smart clinical decision support systems, with future research aimed at validating it on larger and more diverse patient populations.