Machine Learning-Based Early Prediction of Hospital Readmission Risk Among Chronic Disease Patients Using Electronic Health Records: A Comparative Study of Ensemble Learning Models

Abstract

Hospital readmission among patients with chronic diseases remains a major challenge for healthcare systems due to its association with poor patient outcomes and increased healthcare costs. This study proposes a machine learning-based framework for the early prediction of 30-day hospital readmission risk using the publicly available Diabetes 130-US Hospitals dataset from the UCI Machine Learning Repository. A comprehensive preprocessing pipeline, feature engineering, and feature selection techniques were employed to improve data quality and predictive performance. Eight supervised machine learning algorithms, including Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, LightGBM, CatBoost, Multilayer Perceptron, and XGBoost, were developed and comparatively evaluated. Model performance was assessed using accuracy, precision, recall, F1-score, specificity, and the area under the receiver operating characteristic curve (AUC-ROC). The experimental results demonstrated that ensemble learning models consistently outperformed conventional machine learning approaches. Among all evaluated models, XGBoost achieved the best performance, attaining 92.16% accuracy, 0.92 precision, 0.91 recall, 0.91 F1-score, 0.95 specificity, and an AUC-ROC of 0.972. These findings indicate that XGBoost effectively identifies patients at high risk of early hospital readmission and can serve as a reliable predictive tool for clinical decision support. The proposed framework has strong potential for integration with Electronic Health Record systems to facilitate early intervention, improve patient outcomes, reduce preventable readmissions, and support value-based healthcare delivery.