Occupational and Physical Activity Factors as Predictors of Prediabetes: A Machine Learning Study With SHAP-Based Interpretation.

Abstract

A prediabetes prediction model based on nonclinical occupational and physical activity factors was developed to interpret key predictors for early intervention. Data from 33,265 individuals in the Korea National Health and Nutrition Examination Survey were analyzed using logistic regression, decision tree, XGBoost, and random forest algorithms. SHapley Additive exPlanations was used for model interpretation. The random forest model demonstrated the best performance (accuracy: 0.8037; AUC: 0.8597). Professional occupation, decreased average working hours, and increased walking days were associated with reduced prediabetes risk, whereas long working hours and high physical demands were associated with increased prediabetes risk. Occupational and physical activity factors are important nonclinical predictors of prediabetes. These findings support targeted and accessible prevention strategies in settings with limited clinical resources, highlighting the importance of lifestyle in managing diabetes risk.