CIN-RiskNet: A Dynamic Feature-Enhanced TabTransformer with Hybrid SMOTE-Noise Augmentation for Contrast-Induced Nephropathy Prediction

Background Contrast-Induced Nephropathy (CIN) is a serious complication following the use of contrast media in cardiovascular interventions, with no effective treatment available. Early prediction is crucial for prevention, but existing models often struggle with class imbalance, feature redundancy, and noise in clinical data. Methods 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 using five-fold cross-validation on a clinical dataset from Tianjin University Chest Hospital. Results CIN-RiskNet achieved state-of-the-art performance with an accuracy of 99.0%, recall of 99.0%, and an F1-score of 99.0%, outperforming traditional machine learning models such as XGBoost, Random Forest, and support vector machine. Ablation studies confirmed the contributions of each module, demonstrating improved robustness and generalization. Conclusions The proposed model effectively addresses key challenges in CIN prediction, including class imbalance and feature noise, through an integrated deep learning framework. It shows strong potential for clinical application, though further validation on multi-center datasets is recommended to enhance generalizability.