Predictive Analytics for Patient Readmission Using Machine Learning Models

The use of machine learning models for predicting patient readmission risks in this paper is intended to improve healthcare decision. Model evaluation methods such as confusion matrix visualization, F 1 -score calculation, and SHAP (SHapley Additive Explanations) were used to understand the performance and explainability of the model. It shows the impact of changing the prediction thresholds on the prediction accuracy, while the SHAP summary plot illustrates the importance of predictors in predicting hospital readmission. Moreover, a comparative box plot indicating readmission risk across patient subgroups shows differences in demographics, which is important for targeted health care management. Machine learning models could provide new means of prediction to enhance patient outcomes, reduce hospital readmissions and guide precision clinical.