Patient Readmission Risk Prediction: Machine Learning Classification Algorithms
Manish Tripathi, Deependra Rastogi · Universal Research Reports · 2025
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.