Explainable Ensemble Learning Framework with SMOTE, SHAP and LIME for Predicting 30-Day Readmission in Diabetic Patients

Explainable Ensemble Learning Framework with SMOTE, SHAP and LIME for Predicting 30-Day Readmission in Diabetic Patients

diabetes; ensemble learning; explainable AI; machine learning; readmission prediction; SMOTE

Hospital readmission among diabetic patients poses a significant burden on healthcare systems due to its frequency and associated costs. This study presents a machine learning framework for predicting 30-day readmission in diabetic patients using the Diabetes 130-US Hospitals dataset. The framework integrates data preprocessing, SMOTE for class balancing, ensemble learning, and explainable AI (SHAP and LIME) to enhance both accuracy and interpretability. Multiple models were evaluated, and the best performance was achieved by a weighted ensemble with a recall of 89.43% and an F1-score of 0.6612, indicating strong sensitivity. Explainability analysis using SHAP and LIME highlighted key predictors, notably Medication Change Status and Inpatient Admissions, which are clinically relevant. By combining predictive performance with transparent explanations, the proposed framework offers a practical and trustworthy tool for clinical decision support in managing diabetic readmissions

Joshua Pinem1, Widi Astuti2, Adiwijaya3

https://jurnal.iaii.or.id/index.php/RESTI/article/view/6977/1132

Informatics Study Program, School of Computing, Telkom University, Bandung, Indonesia

September 29, 2025

FAJAR BAGUS W

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