A Novel Two-Layer Hybrid Stacked Ensemble for Early-Stage Diabetes Classification

Diabetes is a global epidemic that demands early diagnosis to mitigate its progression and associated complications. By avoiding complicated medical procedures, machine learning presents a potential approach for the precise and efficient identification of diabetes. This study introduces a novel two-layer stacked ensemble model for early-stage diabetes classification. We employed four rigorous feature selection methods to determine the most salient features. Three heterogeneous base learners were then trained using the extracted features, namely Light Gradient Boosting Machine (LGBM), Extreme Gradient Boosting (XGBM), and Categorical Boosting (CatBoost). Finally, these learners were integrated into a stacked ensemble architecture with SVM as the meta-learner. Our proposed model outperformed the existing state-of-the-art models and those reported in previous studies. The model achieved an impressive accuracy of 99.04%, along with a precision of 0.99, a recall of 0.98, an F1-score of 0.975, and a perfect AUC value of 1. These results highlight the effectiveness of the stacked ensemble model in accomplishing highly accurate diabetes prediction, paving the way for improved early intervention and disease management.