Application of machine learning technologies in predicting severity of heat-related illnesses among construction workers

Purpose Exposure to extreme heat poses significant health risks for construction workers due to multiple reasons. However, research on heat-related illnesses (HRIs) in this sector has often relied on statistical methods, limiting insights into causal mechanisms and feature interrelations. This study addresses this gap by employing machine learning (ML) techniques to predict the severity of HRIs (fatal vs non-fatal) and identify critical contributing factors. Design/methodology/approach A total of 370 Occupational Safety and Health (OSHA) HRI reports are analyzed using natural language processing (NLP) to extract key features. These features are combined with climatic and non-climatic variables. Four ML algorithms – random forest, extreme gradient boosting, k-nearest neighbors and logistic regression – are applied to classify HRIs as fatal and non-fatal. Feature importance and dependencies are explored using SHAP (SHapley Additive exPlanations). Findings The random forest model achieves the highest accuracy at 70%. Employers' negligence and workers aged over 35 years are key human-centric factors in fatal HRIs. Humid subtropical and hot-summer Mediterranean climate zones are the most hazardous environmental factors. The results suggest that small-scale projects with limited budgets, renovation projects and building projects (residential/commercial) are the key contributing project-related features in the severity of HRIs. Originality/value This study is among the first attempts to investigate HRI reports specifically in the construction industry by using artificial intelligence techniques. The findings emphasize the need for tailored heat stress prevention programs in construction projects based on their characteristics.