An Explainable AI-Based Ensemble Machine Learning Framework for Early-Stage Diabetes Prediction

• Introduced the Explainable Ensemble Learning Framework (EELF) a Voting Classifier, that integrates Logistic Regression, Random Forest, and K-Nearest Neighbors with optimized hyperparameters to predict diabetes.

• Incorporated SHAP and LIME to enhance model interpretability by identifying key feature contributions, thereby improving clinician trust in the decision-making process.

• Conducted a comparative analysis, where the EELF achieved an accuracy of 81.16%, demonstrating strong potential for clinical application.