The construction industry is among the riskiest industries around the world. Hence, the preliminary studies exploring the consequences of occupational accidents have received considerable attention in research society. This study aims to develop a comprehensive framework to predict the post-accident disability status of construction workers. The dataset comprising 47,938 construction accidents recorded in Turkey was subjected to a detailed multi-step feature engineering approach, including data encoding, data scaling, dimension reduction, and data resampling. Predictions were performed through four tree-based ensemble machine learning models: Random Forest, XGBoost, AdaBoost, and Extra Trees, as well as a state-of-the-art optimization method for hyperparameter tuning, Genetic Algorithm (GA). GA-XGBoost presented the highest prediction rate with 0.8292 in terms of accuracy and 0.8120 with respect to AUROC. The findings may aid in predicting construction workers' post-accident disability status, resulting in a safer working environment and productivity planning in construction projects.

建筑业是世界上风险最高的行业之一。因此，探索职业事故后果的初步研究在研究界受到了相当大的关注。本研究旨在开发一个综合框架来预测建筑工人的事故后残疾状况。包含在土耳其记录的 47,938 起建筑事故的数据集经过详细的多步骤特征工程方法，包括数据编码、数据缩放、降维和数据重采样。预测是通过四种基于树的集成机器学习模型执行的：随机森林、XGBoost、AdaBoost 和额外树，以及用于超参数调整的最先进的优化方法遗传算法 (GA)。GA-XGBoost 的预测率最高，为 0。准确度为 8292，而 AUROC 为 0.8120。研究结果可能有助于预测建筑工人事故后的残疾状况，从而为建筑项目提供更安全的工作环境和生产力规划。