Predictive Modeling of Patient Readmission Risk using Machine Learning: A Comparative Study

Abstract

This research paper presents a comparative study of machine learning (ML) algorithms for predicting patient readmission risk in healthcare settings. Leveraging artificial intelligence (AI) techniques, various ML models including decision trees, random forests, support vector machines, and neural networks are evaluated using electronic health records data. The study aims to identify the most accurate and interpretable model for predicting readmission risk, thereby assisting healthcare providers in implementing proactive interventions to reduce readmission rates and improve patient outcomes