Early-Stage Diabetes Prediction Using a Stacked Ensemble Model Enhanced with SHAP Explainability

Diabetes is one of the most prevalent diseases of our time, and, untreated, it can lead to other health issues. The objective of this research paper is to develop an explainable stacked ensemble model for the early diagnosis of diabetes. The Early-Stage Diabetes Risk Prediction dataset was preprocessed using mean imputation, SMOTE-based class balancing, and mean normalization. A stratified train–test split was applied, and a stacked ensemble model was developed, utilising SHAP and LIME to ensure explainable and interpretable predictions. The proposed model achieved higher performance regarding the Early Stage Diabetes Risk Prediction dataset than did typical models, including Naive Bayes (NB), k-Nearest Neighbour (KNN), Support Vector Machine (SVM), and Decision Tree (DT), with an accuracy of 98.4%. The innovative application of ensemble learning enhances the model's reliability and effectiveness, offering valuable insights for identifying potential diabetic patients. The high accuracy underscores the model's potential as a valuable tool for early detection and treatment, ultimately improving patient outcomes in diabetes management. A critical aspect of our methodology is the integration of SHAP (SHapley Additive exPlanations) and Local Interpretable Model-Agnostic Explanations (LIME), which enhances explainability by revealing the factors driving the model's predictions and highlighting feature importance.