Predictive Modeling of 30-Day Readmission Risk: A Machine Learning Approach for Health Management
Öz
Aim: This study aims to develop and evaluate predictive models capable of identifying patients at risk of 30-day readmission using structured inpatient data.
Material and Method: The analysis was conducted on a fully synthetic dataset designed to reflect the complexity of real-world clinical data while ensuring the protection of personal health information. The dataset includes key features relevant to inpatient care, such as patient demographics, diagnosis codes, laboratory abnormalities, procedural counts, length of stay, and risk scores. A comprehensive preprocessing phase involving label encoding, data balancing, and normalization is completed. In the data preprocessing stage, categorical variables were encoded, class imbalance was corrected using the SMOTE method, and Logistic Regression, Random Forest, and XGBoost models were applied.
Results: According to the results, the Random Forest model demonstrated the highest performance, achieving over 92% accuracy and an AUC value of 0.95. Feature significance analysis revealed that risk score, length of stay, and number of procedures were the most effective variables in determining the risk of readmission. The findings indicate that predictive analytics approaches can make significant contributions to early intervention planning and resource optimization in healthcare management.
Conclusion: The proposed modeling approach may support early intervention planning and hospital resource optimization while promoting data-driven strategies to reduce preventable readmissions.
Anahtar Kelimeler
Inpatient readmission, Predictive modeling, Risk management implications, Clinical decision support, Health management, Machine learning
Kaynakça
- Brady, N., Liang, Y., Seidl, K. L., Marcozzi, D., Stryckman, B., Gingold, D. B. (2024). Association of timely outpatient follow-up and readmission risk in a mobile integrated health program. Population Health Management, 27 (4), 249–256. doi:10.1089/pop.2024.0020
- Bozorgpanah, A., & Torra, V. (2024). Explainable machine learning models with privacy. Progress in Artificial Intelligence, 13, 31–50. https://doi.org/10.1007/s13748-024-00315-2
- Carroll, A. R., Hall, M., Harris, M., Carroll, M. S., Auger, K. A., Davis, M. M., … Williams, D. J. (2025). Validation of 30-day pediatric hospital readmission risk prediction models. JAMA Network Open, 8 (2), e2459684. doi:10.1001/jamanetworkopen.2024.59684
- Cui, S., Gao, R., Kuang, J., Yang, L., Qiu, H., Wei, X. (2025). An interpretable imbalance ensemble classification method for readmission risk assessment incorporating multi-view perturbation and SHAP analysis. Decision Support Systems, 190, Article 114404. doi:10.1016/j.dss.2025.114404
- Durmaz, H., Doğan, M., & Yenilmez, İ. (2024, Aralık). Enhancing explainability in machine learning models: Addressing the Rashomon effect with SHAP and LORE. Sözel bildiri, Selçuk 11th International Conference on Applied Sciences, Konya.
- Gerstmeyer, J., Gorbacheva, A., Avantaggio, A., Pierre, C., Yilmaz, E., Schildhauer, T. A., … Chapman, J. R. (2025). Spine surgery and readmission: Risk factors in lumbar corpectomy patients. North American Spine Society Journal, 21, Article 100587. doi:10.1016/j.xnsj.2025.100587
- Góngora Alonso, S., Herrera Montano, I., Ayala, J. L. M., Rodrigues, J. J. P. C., Franco-Martín, M., de la Torre Díez, I. (2024). Machine learning models to predict readmission risk of patients with schizophrenia in a Spanish region. International Journal of Mental Health and Addiction, 22(4), 2508–2527. doi:10.1007/s11469-022-01001-x
- González-Santa Cruz, A., Mauro, P. M., Sapag, J. C., Martins, S. S., Ruiz-Tagle, J., Gaete, J., … Castillo-Carniglia, A. (2025). Effect of residential versus ambulatory treatment for substance use disorders on readmission risk in a register-based national retrospective cohort. Social Psychiatry and Psychiatric Epidemiology. doi:10.1007/s00127-025-02865-9
- Kwon, J., Farquhar, D., Tasoulas, J., Hackman, T. G. (2024). Post-surgical readmission risk factors in otolaryngology/head and neck surgery. Head and Neck, 46(10), 2407–2414. doi:10.1002/hed.27706
- Li, J., Liang, L., Samuel Cai, Y., Zuo, Y., Su, J., Feng, L., … Tong, Z. (2024). Tracking COPD exacerbation patterns and forecasting readmission risks utilizing electronic medical records. International Journal of Medical Informatics, 189, 105505. doi:10.1016/j.ijmedinf.2024.105505