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Heart disease prediction using hybrid TabNet architecture with stacked ensemble learning
Rizwana Yasmeen1, Lal Khan2, Ahyoung Choi2
1Department of Computer Science, National University of Modern Languages (NUML), Islamabad, Pakistan.
Insights
This study introduces a novel ensemble model combining deep learning and tree-based methods for improved cardiovascular disease (CVD) risk prediction. The new framework enhances early detection accuracy, aiding clinical decision-making.
Area of Science:
- Computational biology
- Medical informatics
- Machine learning in healthcare
Background:
- Cardiovascular diseases (CVDs) are a leading global cause of mortality.
- Current CVD prediction tools face limitations due to data noise and modest accuracy.
- Early detection of CVDs is crucial for effective intervention and patient outcomes.
Purpose of the Study:
- To develop an advanced stacked ensemble framework for enhanced cardiovascular disease risk prediction.
- To integrate deep learning (TabNet) and tree-based (XGBoost) models for improved accuracy and interpretability.
- To provide clinicians with a more reliable tool for early CVD detection.
Main Methods:
- A stacked ensemble framework was developed, integrating TabNet and XGBoost.
- Logistic Regression (LR) or Support Vector Machine (SVM) was employed as a meta-learner.
- The model was evaluated on Kaggle and UCI CVD datasets.
Main Results:
- The proposed ensemble model demonstrated superior performance over baseline models.
- Key performance metrics including accuracy, F1-score, precision, recall, ROC-AUC, PR-AUC, and MCC were significantly improved.
- The framework successfully balanced predictive accuracy with model interpretability.
Conclusions:
- Combining deep learning and tree-based models offers a practical advancement in CVD risk prediction.
- The developed ensemble framework supports clinicians in making more reliable decisions for early CVD detection.
- This approach holds significant potential for improving patient outcomes in cardiovascular health.
Abstract:
Cardiovascular diseases (CVDs) remain the leading cause of death worldwide, and early detection is critical for timely intervention and improved patient outcomes. However, current prediction tools are often limited by noisy, heterogeneous patient data and modest accuracy. To address this challenge, we propose a stacked ensemble framework that integrates: TabNet, a deep learning model that can identify the most relevant clinical features, and XGBoost, a powerful tree-based method known for its robustness. Their outputs are integrated using a Logistic Regression (LR) or Support Vector Machine (SVM) as meta learner, creating a system that balances accuracy and interpretability. Testing on Kaggle and UCI CVD datasets demonstrate that our ensemble consistently outperforms baseline models across accuracy, F1-score, precision, recall, ROC-AUC, PR-AUC, and matthews correlation coefficient (MCC). These results suggest that combining deep learning with tree-based models offers a practical way to improve risk prediction, supporting clinicians in making more reliable decisions for early CVD detection.
