International Journal of Research and Development in Engineering Sciences (IJRDES ) | Predicting Hospital Stay Length Using Explainable Machine Learning

Predicting Hospital Stay Length Using Explainable Machine Learning

Predicting the length of hospital stay (LOS) is critical for improving healthcare resource management and patient care. This study investigates the application of explainable machine learning techniques to forecast hospital stay duration using a dataset from Kaggle, comprising various patient and hospital-related features. The primary goal is to develop accurate predictive models and elucidate the underlying factors influencing hospital stay lengths. The study employs multiple machine learning algorithms, including Logistic Regression, Multi-Layer Perceptron (MLP), Random Forest, Gradient Boosting, and XGBoost. Each model's performance is evaluated using standard metrics such as accuracy, precision, recall, and F1-score. Additionally, explainability tools such as SHapley Additive exPlanations (SHAP) are utilized to interpret model predictions and identify the most significant predictors of LOS. The findings demonstrate that advanced machine learning models, particularly ensemble methods, achieve superior predictive accuracy. Moreover, the explainability analysis provides valuable insights into the critical factors influencing hospital stays, thereby enabling healthcare practitioners to make informed decisions and optimize hospital resource allocation. This research underscores the potential of integrating explainable machine learning into healthcare analytics to enhance operational efficiency and patient outcomes

Research Type: Action Research
Paper Type: Analytical Research Paper
Vol.7 , Issue 4 , Pages: 25 – 30, Aug 2025
Published on: 03 Aug, 2025
Issue Type: Regular

Cite Score

:

100
No. of authors

:

75
No. of Downloads

:

43

Cite Score

:

100
No. of authors

:

75
No. of Downloads

:

43

Cite Score

:

100
No. of authors

:

75
No. of Downloads

:

43

About Authors:

SATHYA LAHARI

India

Viswam Engineering College

"""

Copyright © 2025, This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC-BY-NY-SA). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Corresponding Author: SATHYA LAHARI, sathyalahari0003@gmail.com

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Conflict of interest: The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.