Improved Accuracy of Calculation of Vehicle Crash Severity in Highways using Random Forest over Naive Bayes Algorithm

Authors

  • Vignesh.S
  • Sashi rekha K

DOI:

https://doi.org/10.47750/pnr.2022.13.S04.177

Keywords:

Crash Severity, Novel Random Forest Algorithm, Naive Bayes Algorithm, Machine Learning, Artificial Intelligence.

Abstract

Aim: To improve the accuracy rate of vehicle crash severity in highways using Random forest over Naive Bayes.

Materials and Methods: Random forest and Naive Bayes with sample size of (N=10) is executed with varying training and testing splits for calculating the accuracy for accident crash severity with g power as 75%, threshold 0.000 and confidence interval 95%..
The performance of the classifiers are evaluated based on their accuracy rate using accident severity dataset.

Results: The accuracy for calculating accident crash severity in Random Forest(91%) and Naive Bayes (71%) is obtained(P<0.005).

Conclusion: Prediction of accident crash severity using Random Forest (RF) algorithm appears to be significantly better than Naive Bayes(NVB) with improved accuracy

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Published

2022-10-07

Issue

Vol. 13 SPECIAL ISSUE 04 (2022)

Section

Articles

How to Cite

Improved Accuracy of Calculation of Vehicle Crash Severity in Highways using Random Forest over Naive Bayes Algorithm. (2022). Journal of Pharmaceutical Negative Results, 1479-1485. https://doi.org/10.47750/pnr.2022.13.S04.177