CIN-RiskNet: a dynamic feature-enhanced TabTransformer with hybrid SMOTE-noise augmentation for contrast-induced...

To propose a dynamic feature-enhanced TabTransformer framework that providing a more effective and accurate tool for predicting Contrast-Induced Nephropathy (CIN).

This study proposes CIN-RiskNet, a dynamic feature-enhanced TabTransformer model integrated with a hybrid SMOTE-Noise augmentation strategy. The approach includes adaptive feature gating to suppress noise, synthetic minority oversampling to address class imbalance, and multi-head self-attention to capture complex feature interactions. The model was trained and evaluated under a leakage-free stratified five-fold cross-validation protocol, where SMOTE and Gaussian noise were applied only to the training split within each fold trained on a clinical dataset from Tianjin University Chest Hospital that including a total of 1,679 patients who underwent percutaneous coronary intervention for coronary heart disease.

Under leakage-free five-fold evaluation, CIN-RiskNet achieved strong performance with an accuracy of 95.40%, a recall of 95.40%, and an F1-score of 95.42%. It attained the highest F1-score and recall among all evaluated configurations. It outperformed not only traditional machine learning models including XGBoost, Random Forest, and Support Vector Machine, but also the Mehran risk score, a widely used clinical scoring system for CIN prediction. Ablation studies confirmed the contributions of each module, demonstrating improved robustness and generalization.

The proposed model effectively addresses key challenges in CIN prediction, including class imbalance and feature noise, through an integrated deep learning framework. It shows promising potential as a decision-support tool, while external multicenter validation remains necessary before broad clinical deployment.