Applying Machine Learning to Predict Patient Readmission Rates in Healthcare

The readmission rate in hospitals is the measure of efficiency of the treatment provided by them. The major challenge towards the improvement of health outcomes and proper resource utilization is patient readmission prediction. It is a traditional failure often from methods that do not give accurate identification of at-risk patients thus leading to ineffective therapy. Therefore, a machine learning technique was designed that predicts readmission by various attributes and the context of the hospital stay. This XGBoost implementation presented excellent performance in detecting high-risk people, with an accuracy level of 85% and a recall rate of 83%. These are some indicators that may demonstrate the potential of the proposed method to optimize health care resources, patient outcomes, and reduce readmissions. This paper establishes the groundwork for embedding advanced predictive analytics within the hospital management system.