International Journal of Computer
Trends and Technology

Research Article | Open Access | Download PDF

Volume 73 | Issue 5 | Year 2025 | Article Id. IJCTT-V73I5P125 | DOI : https://doi.org/10.14445/22312803/IJCTT-V73I5P125

Predicting Hospital Readmissions Using Machine Learning: A Data-Driven Approach to Healthcare Optimization

Shantanu Seth

Received Revised Accepted Published
07 Apr 2025 10 May 2025 21 May 2025 31 May 2025

Citation :

Shantanu Seth, "Predicting Hospital Readmissions Using Machine Learning: A Data-Driven Approach to Healthcare Optimization," International Journal of Computer Trends and Technology (IJCTT), vol. 73, no. 5, pp. 196-204, 2025. Crossref, https://doi.org/10.14445/22312803/IJCTT-V73I5P125

Abstract

Hospital readmission is a key issue for health systems, especially in the case of diabetic patients who tend to have multiple and demanding care requirements as well as elevated readmission rates. In this study, machine learning (ML) models to predict diabetic hospital readmission based on an extensive database of 101,766 hospitalizations will be created and tested. The research analyzed several readmission risk-associated factors, such as demographic variables, hospital utilization measures, diabetes-related clinical variables, and medication management patterns. The gradient boosting model performed the best with an area under the receiver operating characteristic curve (AUC-ROC) of 0.78 and an F1-score of 0.71. The analysis revealed that prior inpatient visits, emergency department use, insulin regimen changes, and medication complexity were the most significant predictors of readmission. The results are informative for creating focused interventions to lower readmission rates for diabetic patients and enhance general healthcare quality and resource utilization.

Keywords

Diabetes Readmissions, Machine Learning Healthcare, Predictive Analytics, Hospital Utilization Patterns, Stacked Ensemble Modeling.

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