Comparative Analysis of Machine Learning Models for Predicting Healthcare Traffic: Insights for Optimized Emergency Response

Authors

Shadman Mahmood Khan Pathan, Old Dominion UniversityFollow
Sakan Binte Imran, Sir Salimullah Medical College
M. M. Shabab Iqbal, Kalinga Institute of Industrial Technology
Muhammad Enayetur Rahman, Old Dominion UniversityFollow
Md. Nurul Absar Siddiky, The University of North Carolina at Charlotte
Muhammad Rezaur Rahman, Agile Crafts
Md Rafid Hasan, The University of North Carolina at Charlotte
Nondon Lal Dey, University of Louisiana at Lafayette
MD Sobuj Hossain, University of Louisiana at Lafayette

Document Type

Article

Publication Date

2024

DOI

10.30574/msarr.2024.12.2.0175

Publication Title

Magna Scientia Advanced Research and Reviews

Volume

12

Issue

2

Pages

54-61

Abstract

Efficient management of healthcare traffic is crucial for ensuring timely access to medical services, particularly in emergency situations where delays can have severe consequences. This study presents a comparative analysis of three widely used machine learning models—Linear Regression, Decision Trees, and Random Forests—aimed at predicting healthcare-related traffic volumes. A large dataset from a metropolitan traffic system was used to train and evaluate the models based on key performance indicators, including Mean Squared Error (MSE), R² Score, and computational efficiency. The results reveal that the Random Forest model offers the best performance, achieving higher predictive accuracy and faster execution times compared to the other models. These findings provide valuable insights into the use of machine learning for optimizing healthcare traffic management, potentially enhancing response times and improving patient outcomes.

Rights

© 2024 The Authors. Authors retain the copyright of this article.

This article is published under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) License.

Original Publication Citation

Pathan, S. M. K., Imran, S. B., Iqbal, M. M. S., Rahman, M. E., Siddiky, M. N. A., Rahman, M. R., Hasan, M. R., Dey, N. L., & Hossain, M. S. (2024). Comparative analysis of machine learning models for predicting healthcare traffic: Insights for optimized emergency response. Magna Scientia Advanced Research and Reviews, 12(2), 54-61. https://doi.org/10.30574/msarr.2024.12.2.0175

Repository Citation

Pathan, Shadman Mahmood Khan; Imran, Sakan Binte; Iqbal, M. M. Shabab; Rahman, Muhammad Enayetur; Siddiky, Md. Nurul Absar; Rahman, Muhammad Rezaur; Hasan, Md Rafid; Dey, Nondon Lal; and Hossain, MD Sobuj, "Comparative Analysis of Machine Learning Models for Predicting Healthcare Traffic: Insights for Optimized Emergency Response" (2024). Electrical & Computer Engineering Faculty Publications. 503.
https://digitalcommons.odu.edu/ece_fac_pubs/503

ORCID

0009-0004-0421-5540 (Rahman)