Comparative analysis of machine learning models for predicting healthcare traffic: Insights for optimized emergency response

Shadman Mahmood Khan Pathan 1, Sakan Binte Imran 2, M M Shabab Iqbal 3, Muhammad Enayetur Rahman 1, Md Nurul Absar Siddiky 4, *, Muhammad Rezaur Rahman 5, Md Rafid Hasan 6, Nondon Lal Dey 7 and MD Sobuj Hossain 8

1 Department of Electrical and Computer Engineering, Old Dominion University, Norfolk, VA- 23529, USA.
2 Department of Medicine, Sir Salimullah Medical College, Dhaka, Bangladesh.
3 Department of Civil Engineering, Kalinga Institute of Industrial Technology, India.
4 Department of Electrical and Computer Engineering, UNC at Charlotte, Charlotte, NC-28213, USA.
5 Department of Research and Innovation, Agile Crafts, Khilgaon, Dhaka-1219, Bangladesh.
6 Department of Physics and Optical Science, UNC at Charlotte, Charlotte, NC-28213, USA.
7 Department of Informatics, University of Louisiana at Lafayette, Lafayette, LA-70506, USA.
8 Department of Physics, University of Louisiana at Lafayette, Lafayette, LA-70506, USA.
 
Research Article
Magna Scientia Advanced Research and Reviews, 2024, 12(02), 054–061
Article DOI: 10.30574/msarr.2024.12.2.0175
Publication history: 
Received on 10 September 2024; revised on 03 November 2024; accepted on 06 November 2024
 
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.
 
Keywords: 
Healthcare traffic management; Machine learning; Random Forest; Traffic prediction; Emergency medical services; Predictive modeling; Urban traffic systems
 
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Copyright information: 
Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0