A novel framework for diabetic risk prediction using SCAW-Net integrated with TabNet architecture.

Abstract

Blood glucose levels are essential for metabolism and brain function; insulin regulates sugar to prevent hypo- and hyperglycemia. Proper control prevents diabetic complications from insulin deficiency or resistance. Rapid, precise diabetes identification is critical for effective care. This study proposes SCAW-Net within TabNet to boost prediction accuracy and computational speed, compared with AdaBoost, XGBoost, Bagging, and Random Forest. Trained on diabetes features and tested on multiple datasets, the model achieved 98.9% accuracy, outperforming others. Consistent results on complex, imbalanced data validate SCAW-Net in TabNet as a promising real-world diabetes prediction tool, supporting timely clinical intervention and improved patient management outcomes.