Abstract: Hospital readmission represents one of the most complex challenges in modern healthcare management, contributing significantly to increased operational costs, inefficient resource utilization, and patient dissatisfaction. This paper presents an Artificial Intelligence (AI)-driven Discharge and Readmission Prevention System designed to predict the probability of patient readmission using advanced machine learning models such as Random Forest and XGBoost. Unlike traditional methods that rely primarily on linear regression models and manual discharge procedures, the proposed system integrates real-time clinical data, patient medical history, and post-discharge behavioral indicators to generate accurate and explainable predictions. The framework emphasizes interpretability through explainable AI techniques, enabling clinicians to understand key contributing factors influencing readmission risk. By leveraging data-driven insights, the system aims to reduce preventable readmissions, enhance hospital workflow efficiency, and improve overall patient care quality. Experimental evaluations conducted on multiple healthcare datasets demonstrate the system’s superior predictive accuracy and scalability, showcasing its potential for real-world deployment in diverse hospital management environments.

Keywords: Artificial Intelligence, Machine Learning, Electronic Health Record, Healthcare Informatics, Predictive Analytics, Explainable AI

Downloads: | DOI: 10.17148/IJIREEICE.2025.131107