Injury Probability Prediction Modeling Using Decision-Tree-Based Machine Learning Models

Abstract

Various machine learning models have been proposed to predict injuries to vehicle occupants from crash conditions, and optimized and evaluated for model accuracy using binary classification performance metrics. However, performance metrics for injury probability prediction have not been utilized to develop injury prediction models. Therefore, this paper developed injury probability prediction models using evaluation metrics to evaluate the probability prediction performance of injury prediction models and to verify the validity of injury probability predictions. The model constructed using a random forest performed better than the conventional injury prediction model constructed using logistic regression.