HybridTabNet-QC: A Transformer-Based Clinical Feature Fusion Framework for Heart Disease Risk Prediction

Abstract

Heart disease remains the leading cause of mortality worldwide, highlighting the need for accurate and interpretable risk prediction models. This study proposes Hybrid Tabular Network with Clinical meta-features and Quality-Control Mechanisms (HybridTabNet-QC), a hybrid deep learning framework designed to enhance clinical decision support systems. By combining a transformer-based tabular encoder with medically engineered meta-features, the model delivers interpretable, generalizable, and noise-robust predictions for heart disease risk. Unlike conventional deep learning models that treat features as raw inputs, HybridTabNet-QC fuses attention-based learning with domain-specific indicators, such as body mass index (BMI), pulse pressure, and cholesterol-glucose interaction scores that ensure predictions are both statistically sound and clinically grounded. The model was evaluated on two publicly available datasets, the Heart Disease dataset and the Cardiovascular Disease dataset from Kaggle. On the combined dataset, HybridTabNet-QC achieved 90.1% accuracy, 90.0% F1-score, and 93.6% AUC-ROC, outperforming traditional machine learning and standard deep learning baselines. The framework demonstrated strong robustness under feature noise and consistent generalization on external validation subsets. Interpretability analyses using LIME and SHAP confirmed that the model prioritizes clinically relevant features, supporting its suitability for real-world clinical decision support systems. These findings demonstrate the model's suitability for deployment in real-world healthcare environments where interpretability, robustness, and scalability are critical.

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