Characteristics of fatal occupational injuries in migrant workers in South Korea: A machine learning study.

Objective: Analysis of occupational injuries is essential for developing preventive strategies. However, few studies have evaluated severe occupational injuries in migrant workers from the perspective of gender. Therefore, using a new analytical method, this study was performed to identify gender-specific characteristics associated with fatal occupational injuries among migrant workers; the interactions between these factors, were also analyzed. In addition, we compared the utility of explainable artificial intelligence (XAI) using SHapley Additive exPlanations (SHAP) with logistic regression (LR) and discuss caveats regarding its use. Materials and methods: We analyzed national statistics for occupational injuries among migrant workers ( Results: We found clear gender differences in fatal occupational injuries among migrant workers, with males in the same occupation having a higher risk of death than females. These gender differences suggest the need for gender-specific occupational injury prevention interventions for migrant workers to reduce the mortality rate. Occupation was a significant predictor of death among female migrant workers only, with care jobs having the highest fatality risk. The occupational fatality risk of female workers would not have been identified without the performance of detailed job-specific analyses stratified by gender. The major advantages of SHAP identified in the present study were the automatic identification and analysis of interactions, ability to determine the relative contributions of each feature, and high overall performance. The major caveat when using SHAP is that causality cannot be established. Conclusion: Detailed job-specific analyses stratified by gender, and interventions considering the gender of migrant workers, are necessary to reduce occupational fatality rates. The XAI approach should be considered as a complementary analytical method for epidemiological studies, as it overcomes the limitations of traditional statistical analyses.