Multi-Class Prediction of Occupational Accident Types Using XGBoost in Pipeline Construction

Abstract: This study uses the XGBoost algorithm to predict accident types in pipeline construction projects through a multi-class classification approach. The model was trained on 1,184 real accident records containing information such as accident time, type, and required treatment. Results showed 83% overall accuracy, 0.82 macro-average F1 score, and 0.81 weighted F1 score. While some classes achieved high recall, categories with limited data performed lower. The findings indicate that XGBoost can support accident type prediction and risk analysis in construction projects using historical incident data.