Hospital Readmission Risk Prediction Using Ensemble Learning

Mangalgouri P Kademani, Yuvaraj P Rathod, Shruti Nagave, Omkar Harlapur, Uday Kulkarni, Shashank Hegde

2025

Abstract

The study focuses on features that affect of hospital readmission’s and explores how advanced machine learning algorithms can predict the chances of hospital readmission’s. Readmissions are caused by early patient discharge, improper discharge planning, and lack of treatment, which lead to de-creased health outcomes, and higher costs. In this study, the patient data is used from the CMS Hospital Readmissions Reduction Program to create prediction models for hospital readmission risk. which includes over 18774 records and 12 columns from 2019 to 2022. The machine learning models, such as MLP, XGBoost, CatBoost, and ensemble, were used to improve the prediction’s. Where MLP achieved the accuracy of 82.69%, and XGBoost and CatBoost outperformed MLP with scores of 85.43% and 86.50%. The accuracy of 87.08% is achieved by ensemble model, which combined the output of all base model’s prediction outputs. Performance matrices which includes precision, recall, F1-score were evaluated in addition to accuracy, the ensemble model obtained precision of 87.48%, recall of 87.08% , and F1-score of 86.38%. The outcomes show the result of the ensemble approach in resolving the complex issue of hospital readmission prediction.

Paper Citation

in Harvard Style

Kademani M., Rathod Y., Nagave S., Harlapur O., Kulkarni U. and Hegde S. (2025). Hospital Readmission Risk Prediction Using Ensemble Learning. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 820-826. DOI: 10.5220/0013603200004664

in Bibtex Style

@conference{incoft25,
author={Mangalgouri P Kademani and Yuvaraj P Rathod and Shruti Nagave and Omkar Harlapur and Uday Kulkarni and Shashank Hegde},
title={Hospital Readmission Risk Prediction Using Ensemble Learning},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT},
year={2025},
pages={820-826},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013603200004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 2: INCOFT
TI - Hospital Readmission Risk Prediction Using Ensemble Learning
SN - 978-989-758-763-4
AU - Kademani M.
AU - Rathod Y.
AU - Nagave S.
AU - Harlapur O.
AU - Kulkarni U.
AU - Hegde S.
PY - 2025
SP - 820
EP - 826
DO - 10.5220/0013603200004664
PB - SciTePress