Improved Accuracy of Calculation of Vehicle Crash Severity in Highways using Random Forest over Naive Bayes Algorithm (Journal of Pharmaceutical Negative Results, 2022)
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