Metabolic risk stratification of night shift workers in a large retail workplace through clustering and SHAP interpretation

It has been confirmed that the metabolic health risks of night workers vary from person to person, and that these differences can be effectively classified (tiered) through machine learning analysis. The results of this study serve as an important foundation for developing customized industrial health strategies, such as redesigning work schedules or personalized health care and prevention programs.