Patient Readmission Risk Prediction: Machine Learning Classification Algorithms

Manish Tripathi; Dr. Deependra Rastogi

doi:10.36676/urr.v12.i2.1526

Authors

Manish Tripathi Cornell University Ithaca, New York, USA
Dr. Deependra Rastogi IILM University, Greater Noida, Uttar Pradesh 201306, India

DOI:

https://doi.org/10.36676/urr.v12.i2.1526

Keywords:

Patient readmission, machine learning, classification algorithms, logistic regression, decision trees, support vector machines, random forests, predictive modeling, healthcare, early intervention, risk prediction, hospital management, patient care, medical data analysis

Abstract

Predicting patient readmission risk is key to improving healthcare, cutting costs, and making hospital operations more efficient. As hospitals work to provide quality care with limited resources, accurately identifying which patients are likely to be readmitted has become a priority. This study explores how machine learning models—like logistic regression, decision trees, support vector machines, and random forests—can help. Using patient data such as demographics, medical history, treatment patterns, and hospital factors, the models identify the main drivers of readmission risk. By comparing their performance through metrics like accuracy, precision, recall, and ROC curve scores, the study highlights the best-performing approach, showing how machine learning can support early interventions, improve care, and reduce unnecessary readmissions.

References

• Choi, E., Schuetz, A., Stewart, W. F., & Sun, J. (2016). Using recurrent neural networks for early detection of heart failure readmissions. Proceedings of the 2016 SIAM International Conference on Data Mining (SDM), 286-294. https://doi.org/10.1137/1.9781611974348.35

• Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347-1358. https://doi.org/10.1056/NEJMra1814259

• Zhao, J., Xie, P., & Dong, W. (2019). Predicting hospital readmission risk using machine learning models: A case study on a large healthcare dataset. Journal of Healthcare Engineering, 2019, Article 8091854. https://doi.org/10.1155/2019/8091854

• Shah, N. D., & Jensen, P. B. (2020). Applications of machine learning in the prediction of hospital readmissions. JAMA, 323(9), 879-880. https://doi.org/10.1001/jama.2020.0817

• Ramos, R., & Santos, J. (2021). Leveraging machine learning for patient risk assessment in hospitals: A systematic review of approaches and challenges. Healthcare, 9(2), 178. https://doi.org/10.3390/healthcare9020178

• Choudhury, M., & Sheth, A. (2017). Predictive modeling for readmission prediction in hospitals: A review of approaches and techniques. International Journal of Healthcare Information Systems and Informatics, 12(4), 1-20. https://doi.org/10.4018/IJHISI.2017100101

• Liu, J., & Zhang, Y. (2020). A comprehensive study on patient readmission prediction: Machine learning techniques and their clinical applications. Journal of Healthcare Analytics, 5(3), 121-133. https://doi.org/10.1080/2474128X.2020.1800906

• López, G., & Hernández, M. (2021). Bias and fairness in healthcare machine learning models: A systematic review. Journal of Biomedical Informatics, 113, 103642. https://doi.org/10.1016/j.jbi.2020.103642

• Singh, K., & Kumar, D. R. (2025). Performance Tuning for Large-Scale Snowflake Data Warehousing Solutions. Journal of Quantum Science and Technology (JQST), 2(1), Jan(1–21). Retrieved from https://jqst.org/index.php/j/article/view/149

• Ramdass, Karthikeyan, and S. P. Singh. 2024. “Innovative Approaches to Threat Modeling in Cloud and Hybrid Architectures.” International Journal of Research in All Subjects in Multi Languages 12(11):36. Resagate Global - Academy for International Journals of Multidisciplinary Research. Retrieved (www.ijrsml.org).

• Incremental Policy Compilation for Fine-Grained Security Enforcement in Federated Data Centers , IJCSPUB - INTERNATIONAL JOURNAL OF CURRENT SCIENCE (www.IJCSPUB.org), ISSN:2250-1770, Vol.9, Issue 1, page no.57-78, February-2019, Available :https://rjpn.org/IJCSPUB/papers/IJCSP19A1008.pdf

• Katyayan, Shashank Shekhar, and S.P. Singh. 2025. Optimizing Consumer Retention Strategies Through Data-Driven Insights in Digital Marketplaces. International Journal of Research in All Subjects in Multi Languages 13(1):153. Resagate Global - Academy for International Journals of Multidisciplinary Research. Retrieved (www.ijrsml.org).

• Desai, Piyush Bipinkumar, and Vikhyat Gupta. 2024. Performance Tuning in SAP BW: Techniques for Enhanced Reporting. International Journal of Research in Humanities & Social Sciences 12(10): October. ISSN (Print) 2347-5404, ISSN (Online) 2320-771X. Resagate Global - Academy for International Journals of Multidisciplinary Research. Retrieved from www.ijrhs.net.

• Ravi, Vamsee Krishna, Vijay Bhasker Reddy Bhimanapati, Pronoy Chopra, Aravind Ayyagari, Punit Goel, and Arpit Jain. (2022). Data Architecture Best Practices in Retail Environments. International Journal of Applied Mathematics & Statistical Sciences (IJAMSS), 11(2):395–420.

• Gudavalli, Sunil, Srikanthudu Avancha, Amit Mangal, S. P. Singh, Aravind Ayyagari, and A. Renuka. (2022). Predictive Analytics in Client Information Insight Projects. International Journal of Applied Mathematics & Statistical Sciences (IJAMSS), 11(2):373–394.

• Jampani, Sridhar, Vijay Bhasker Reddy Bhimanapati, Pronoy Chopra, Om Goel, Punit Goel, and Arpit Jain. (2022). IoT Integration for SAP Solutions in Healthcare. International Journal of General Engineering and Technology, 11(1):239–262. ISSN (P): 2278–9928; ISSN (E): 2278–9936. Guntur, Andhra Pradesh, India: IASET.

• Goel, P. & Singh, S. P. (2009). Method and Process Labor Resource Management System. International Journal of Information Technology, 2(2), 506-512.