Document Type

Article

Publication Date

2024

DOI

10.29322/IJSRP.14.09.2024.p15305

Publication Title

International Journal of Scientific and Research Publications

Volume

14

Issue

9

Pages

20-28

Abstract

Effective healthcare traffic management is critical for ensuring prompt medical services, particularly in emergencies where delays can have life-threatening consequences. This study conducts a comparative analysis of three popular machine learning models—Linear Regression, Decision Trees, and Random Forests—for predicting healthcare-related traffic volumes. Utilizing a comprehensive dataset from a metropolitan interstate traffic system, the models were evaluated based on key performance metrics, including Mean Squared Error (MSE), R² Score, and execution time. The findings demonstrate that the Random Forest model outperforms the others, offering superior predictive accuracy and efficiency. These insights are valuable for optimizing traffic management in healthcare, ultimately contributing to improved patient outcomes.

Rights

© 2024 The Authors.

This publication is licensed under a 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., Hassan, M. R., Dey, N. L., & Hossain, M. S. (2024). Predictive modeling of healthcare traffic using machine learning: A comparative study. International Journal of Scientific and Research Publications, 14(9), 20-28. https://doi.org/10.29322/IJSRP.14.09.2024.p15305

ORCID

0009-0004-0421-5540 (Rahman, Muhammad E.)

Download

Share

COinS