Abstract

Coronary Artery Disease (CAD) is one of the significant diseases in the field of cardiovascular science where timely identification of the risk will enable the diagnosis of CAD and its proper treatment. The advent of clinical data, laboratory data, and electrocardiographic data has led to the creation of artificial intelligence-based systems to predict CAD. Although traditional machine learning models are not very efficient in understanding the interactions among the features, on the other hand, the deep learning model provides good predictive capabilities but less interpretability. The existing CAD prediction approaches do not pay much attention to clinical transparency, feature interaction, calibration, and prediction explanation. The above-discussed problem makes the CAD prediction approaches unsuitable for the clinical environment. In this work, we propose a new Explainable Deep Cardiac Transformer (EDCT) that integrates deep feature learning, self-attention mechanism, residual feature refinement, and explainability for predicting CAD. As the proposed framework, we first preprocess and normalize the features followed by the deep cardiac feature embedding. The proposed transformer-based attention mechanism learns the interactions and long range dependencies between the heterogeneous clinical and cardiac features. Then, the obtained representations are fed into the classification layers to predict the CAD risk and identify the contribution of each clinical feature to the particular prediction using SHAP-based explanation analysis. According to the experiments, it was found that our proposed EDCT framework has outperformed all the existing approaches in all metrics. At the full training-data proportion, our EDCT framework gives 89.8% accuracy, 87.8% precision, 90.6% recall, 89.3% F1-score, and 0.938 ROC-AUC. Compared with XGBoost, the improvement of EDCT is 3.7%, 3.2%, 3.5%, 3.5%, and 0.032 for accuracy, precision, recall, F1-score, and ROC-AUC, respectively. Compared with AutoGluon Ensemble, the corresponding improvements are 2.5%, 2.1%, 2.4%, 2.4%, and 0.018, respectively. The experimental results show that the self-attention-based cardiac representation learning effectively captures interactions between the heterogeneous clinical and cardiac features.

Authors

R. Rajavignesh¹, G. Venkataramana Sagar²
K.S.K College of Engineering and Technology, India¹, G Pulla Reddy Engineering College, India²

Keywords

Coronary Artery Disease, Cardiac Transformer, Deep Learning, Explainable Artificial Intelligence, Risk Prediction