An improved XGBoost model to predict the injury severity of person in road crash

Abstract

Road traffic accidents are common cause for the human deaths in recent years. The injury severity of person prediction in the road traffic accident is important to find the relevant factors associated with crash. Prediction of injury severity of person in road traffic accident is considered as an important & crucial analysis for driving decisions under the dangerous situations. This study aims to proposes the framework to classify the different classes like No Apparent Injury(O), Possible Injury(C), Suspected Minor Injury (B), Suspected Serious Injury (A), Fatal Injury (K) for injury severity during road crash. In this research paper an improved XGBoost algorithm is used to predict injury severity of person for improving performance like accuracy and loss. The National Highway Traffic Safety Administration (NHTSA) Fatality Analysis Reporting System (FARS) (2017–2019) dataset is used for evaluation of the performance metrics for proposed framework. Initially the dataset is pre-processed by removing missing values and then correlation features selection method is used to find the important strongly correlated features with target Injury Severity. Finally the 44 important attributes are used for training dataset and Injury Severity as Target. The different models like Multinomial Logistic Regression (LR), Extra Trees, Random forest, Naive Bayes and Neural networks, XGB Classifier, improved XGBoost are compared on accuracy, Loss and F1-score metrics. It is seen that improved XGBoost model performs better with 84.06% accuracy and 0.381 loss metric. The study helps to reduce the road accidents by analysing the risk involved in accidents by extent of prediction of injury of person in road accidents.