<![CDATA[This research focuses on optimizing and assessing ensemble learning models for predicting diabetes progression by combining hyperparameter tuning and explainable artificial intelligence techniques. Experiments were conducted using the scikit-learn diabetes dataset, which contains 442 samples with ten numerical features representing patients’ clinical conditions. The data were split into 80% for training and 20% for testing. Two ensemble methods were explored: Random Forest Regressor (bagging) and XGBoost Regressor (boosting). Hyperparameter optimization was carried out using RandomizedSearchCV and BayesianSearchCV under a five-fold cross-validation scheme. Model performance was evaluated using MAE, MSE, RMSE, and R² metrics, while interpretability was examined through SHAP summary plots. The results indicate that BayesianSearchCV consistently delivered superior performance gains compared to random search. In particular, the optimized XGBoost model achieved an R² score of 0.5018, improving by 19.8% over the baseline model (R² = 0.4188), and reduced RMSE from 55.49 to 51.37. SHAP analysis showed that serum triglycerides, body mass index, and blood pressure were the most influential features. Overall, the findings suggest that Bayesian-based hyperparameter optimization can effectively improve ensemble regression performance in medical prediction tasks involving limited datasets.]]>
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