Patient Re Admission Prediction using Machine Learning and Data Science

K. Madhavi; M. Sunita; H. Vanaja; R Nithin Kumar; K. Niharika

Patient Re Admission Prediction using Machine Learning and Data Science

Author(s)	Ms. K. Madhavi, M. Sunita, H. Vanaja, R Nithin Kumar, K. Niharika
Country	India
Abstract	Hospital readmission is a major challenge for healthcare systems because it increases costs, strains resources, and often signals gaps in patient care. Predicting which patients are at high risk of being readmitted can help hospitals take early preventive actions. This study focuses on using machine learning and data science techniques to build a reliable patient readmission prediction model. Clinical and administrative data such as patient demographics, diagnosis history, length of stay, lab results, and prior admissions are analyzed and cleaned before modeling. Feature selection methods are applied to identify the most meaningful variables that influence readmission risk. Several algorithms, including logistic regression, decision trees, random forests, and gradient boosting, are trained and compared to find the most accurate and stable performer. Model evaluation is carried out using metrics such as accuracy, precision, recall, and AUC score to ensure balanced performance. The results show that ensemble models generally provide +stronger predictive power than single models, especially when handling complex and high-dimensional healthcare data. The final system can support clinicians by flagging high-risk patients before discharge, allowing targeted follow- ups and care planning. This approach demonstrates how practical data science methods can improve decision-making and contribute to better patient outcomes and more efficient healthcare delivery.
Keywords	Patient Readmission Prediction, Machine Learning, Data Science, Healthcare Analytics, Predictive Modeling, Hospital Management.
Field	Engineering
Published In	Volume 7, Issue 4, April 2026
Published On	2026-04-04

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Patient Re Admission Prediction using Machine Learning and Data Science

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