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ISSN (Print): 1872-2121
ISSN (Online): 2212-4047

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Research Article

Application of Machine Learning Predicting Injuries in Traffic Accidents through the Application of Random Forest

Author(s): Veer Bhadra Pratap Singh*, V. Hemamalini, Appala Srinuvasu Muttipati, SSSV Gopala Raju, Abu Hena Md Shatil and Abhishek Sharma

Volume 19, Issue 2, 2025

Published on: 04 December, 2023

Article ID: e041223224189 Pages: 14

DOI: 10.2174/0118722121248202231003064459

Price: $65

Abstract

Background: The objective of this work is to analyze and predict the harmfulness in traffic accidents.

Method: Several Random Forest statistical models are created, in which the predictable variable (response/ output variable) is the harmfulness of the accident, while the input variables are the various characteristics of the accident. In addition, these generated models will allow estimating the influence or importance of each of the factors studied (input variables) concerning the harmfulness of road accidents so that it is possible to know in which aspects it is more profitable to work with the objective of reducing mortality from traffic accidents [1].

Results: In this regard, the predictive algorithm has an out-of-bag error of 26.55% and an overall accuracy of 74.1%. Meanwhile, the local accuracy of the mildly wounded class is 66.1% compared to 81.4% of the dead and severely wounded class, which, as mentioned, has higher prediction reliability.

Conclusion: Finally, it is worth noting the enormous usefulness of the Random Forest machine learning technique, which provides very useful information for possible research or studies that may be carried out. In the specific case of this patent work, through the use of the R programming language, which in turn presents a wide range of freely accessible utilities and functions with which it may be interesting working, it has generated results of great value for this area of activity, important to society as road safety.

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