Road traffic accident determinant factor identification in case of East Gojjam, Ethiopia using wrapper feature selection algorithm

Abstract

One of the biggest global challenges to development and public health is road traffic accidents (RTAs). As a result, this study focuses on analysing road traffic accident determinant factors using the Wrapper Feature Selection Method in case of East Gojjam Zone located in Amhara region, Ethiopia, sub-Saharan. To do this, East Gojjam Road traffic office RTA data classified as simple injury, major injury, and death is gathered. The gathered information is pre-processed before being used using machine learning classification algorithms including Nearest Neighbour (KNN), Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), and Naïve Bayes (NB). Using the wrapper feature selection approach, the most significant factor was identified using the machine-learning algorithm KNN, which obtained the best classification score with an accuracy of 99.5 %. Thus, the type of vehicle, the reason for the accident, the location of the accident, and the licence level were identified as crucial RTA factors. Finally, the variables, Sino track, unfavourable weather, Dolphin, and Debre Elias rated 100 %, 100 %, 85 %, and 82.35 % for fatality in relation to the factors licence driver, cause of accident, type of vehicle, and accident location, respectively.