International Journal of Science, Engineering and TechnologyAuthors: Gaurav Saini, Parkh Kumar, Nikhil Pal, Prashant Kumar, Assistant Professor Rimmy
Abstract: Hospital readmission prediction has emerged as a major research area in the healthcare sector because unplanned patient readmissions significantly increase healthcare expenditure, reduce hospital efficiency, and negatively affect patient outcomes. Readmission generally occurs when a patient is admitted to the hospital again within a short duration after discharge, often due to incomplete recovery, poor follow-up care, or chronic medical conditions. Identifying patients who are at high risk of readmission before discharge is therefore essential for improving treatment quality and optimizing hospital resource management. Traditional healthcare prediction methods are often limited in handling large and complex medical datasets, whereas machine learning techniques provide more efficient and intelligent solutions for predictive healthcare analytics. This research paper presents a machine learning-based hospital readmission prediction framework using the XGBoost classifier. The proposed framework is designed to analyze healthcare datasets and predict whether a patient is likely to be readmitted after discharge. The system includes several important stages such as data collection, data preprocessing, feature engineering, model training, model evaluation, and deployment. During preprocessing, missing values, duplicate records, and inconsistent data are handled to improve data quality. Feature engineering techniques are applied to extract meaningful information from patient records, including demographic details, diagnosis history, laboratory reports, medications, and previous admission records. The XGBoost algorithm is used as the primary classification model because of its high efficiency, scalability, and superior predictive accuracy compared to traditional machine learning algorithms. The trained model is evaluated using various performance metrics such as accuracy, precision, recall, F1-score, and ROC-AUC score to measure its predictive capability and reliability. To provide a practical implementation of the proposed framework, the prediction model is deployed using the Streamlit framework, which allows the development of an interactive and user-friendly web application. Healthcare professionals can use the application to input patient information and instantly obtain readmission risk predictions. This real-time prediction capability can assist doctors and hospital administrators in making informed decisions regarding patient discharge planning, follow-up treatment, and preventive care strategies. Experimental results demonstrate that the proposed machine learning framework achieves strong predictive performance and can effectively identify high-risk patients before discharge. The framework has the potential to reduce avoidable hospital readmissions, lower healthcare costs, improve hospital efficiency, and enhance the overall quality of patient care. Furthermore, this research highlights the growing importance of artificial intelligence and machine learning technologies in modern healthcare systems. Future enhancements may include the integration of deep learning models, real-time electronic health record systems, and cloud-based healthcare analytics for improved scalability and prediction accuracy.
