From Baseline to Best Practice: An Advanced Feature Selection, Feature Resampling and Grid Search Techniques to Improve Injury Severity Prediction

This work addresses the need for precise prediction models that predict the severity of injuries sustained in traffic crashes as a regression task. To this end, we thoroughly analyzed traffic crashes in Rome between 2016 and 2019, gathering data on vehicle attributes and environmental factors. Fourth predictive systems are employed to investigate the intricate problem of predicting the severity of injuries sustained in traffic crashes using different regression algorithms, such as Random Forest, Decision Trees, XGBoost, and Artificial Neural Networks. Compared to comparable systems without feature selection, feature resampling, and optimization methods, the results demonstrate that employing optimized XGBoost along with grid search in conjunction with SelectKBest and SMOTE strategy has resulted in greater performance, with an 89% R2 score. Our findings provide insight into the requirement for accurate forecasting models in optimization and balanced approaches to enhancing traffic safety. These findings offer a viable way to improve traffic safety tactics. As far as we know and as of right now, there hasn't been much interest in supporting a fusion-based system that critically reviews machine learning techniques using grid search optimization, feature selection, and smote technique and examines how injury severity prediction is affected by road crashes.