International journal of public health · 2026
Development and external validation of an interpretable machine learning model for obesity-depression comorbidity in Korean and US adults.
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Shangguan Yuwen, Lin Zhenhao, Sim Young-Je, Wu Kunpeng, Chu Yu, Huang Kunyi, Chen Fangxi, Ji Kangkang, Chen Fang, Liu Shangrui
Аннотация
OBJECTIVE: To investigate the association between physical inactivity and obesity-depression comorbidity (ODC), defined as the co-occurrence of obesity and depression, and to develop an effective screening tool for identifying high-risk individuals to facilitate early intervention.
METHODS: Data were obtained from 3,357 physically inactive adults enrolled in the Korea National Health and Nutrition Examination Survey (KNHANES, 2007-2012). An XGBoost machine learning framework was applied to develop predictive models. Feature selection was conducted using random forest, and the prediction mechanism was interpreted with SHAP values. The model was validated internally using KNHANES 2011-2012 data and externally with the U.S. NHANES dataset.
RESULTS: The XGBoost model demonstrated good discriminative performance in internal validation (AUC = 0.783 and 0.744) and achieved an external validation AUC of 0.886. Feature importance analysis revealed that insulin concentration, white blood cell count, and height were the primary predictors of ODC, with insulin exerting the strongest influence.
CONCLUSION: This study developed a high-performing and interpretable prediction model for ODC risk. SHAP-based interpretation identified insulin as the most influential predictor within the model, suggesting that metabolic factors may be important for ODC risk stratification.
METHODS: Data were obtained from 3,357 physically inactive adults enrolled in the Korea National Health and Nutrition Examination Survey (KNHANES, 2007-2012). An XGBoost machine learning framework was applied to develop predictive models. Feature selection was conducted using random forest, and the prediction mechanism was interpreted with SHAP values. The model was validated internally using KNHANES 2011-2012 data and externally with the U.S. NHANES dataset.
RESULTS: The XGBoost model demonstrated good discriminative performance in internal validation (AUC = 0.783 and 0.744) and achieved an external validation AUC of 0.886. Feature importance analysis revealed that insulin concentration, white blood cell count, and height were the primary predictors of ODC, with insulin exerting the strongest influence.
CONCLUSION: This study developed a high-performing and interpretable prediction model for ODC risk. SHAP-based interpretation identified insulin as the most influential predictor within the model, suggesting that metabolic factors may be important for ODC risk stratification.