Using interpretable machine learning methods to identify the relative importance of lifestyle factors for overweight and obesity in adults: pooled evidence from CHNS and NHANES

Abstract

Abstract Background Overweight and obesity pose a huge burden on individuals and society. While the relationship between lifestyle factors and overweight and obesity is well-established, the relative contribution of specific lifestyle factors remains unclear. To address this gap in the literature, this study utilizes interpretable machine learning methods to identify the relative importance of specific lifestyle factors as predictors of overweight and obesity in adults. Methods Data were obtained from 46,057 adults in the China Health and Nutrition Survey (2004–2011) and the National Health and Nutrition Examination Survey (2007–2014). Basic demographic information, self-reported lifestyle factors, including physical activity, macronutrient intake, tobacco and alcohol consumption, and body weight status were collected. Three machine learning models, namely decision tree, random forest, and gradient-boosting decision tree, were employed to predict body weight status from lifestyle factors. The SHapley Additive exPlanation (SHAP) method was used to interpret the prediction results of the best-performing model by determining the contributions of specific lifestyle factors to the development of overweight and obesity in adults. Results The performance of the gradient-boosting decision tree model outperformed the decision tree and random forest models. Analysis based on the SHAP method indicates that sedentary behavior, alcohol consumption, and protein intake were important lifestyle factors predicting the development of overweight and obesity in adults. The amount of alcohol consumption and time spent sedentary were the strongest predictors of overweight and obesity, respectively. Specifically, sedentary behavior exceeding 28–35 h/week, alcohol consumption of more than 7 cups/week, and protein intake exceeding 80 g/day increased the risk of being predicted as overweight and obese. Conclusion Pooled evidence from two nationally representative studies suggests that recognizing demographic differences and emphasizing the relative importance of sedentary behavior, alcohol consumption, and protein intake are beneficial for managing body weight status in adults. The specific risk thresholds for lifestyle factors observed in this study can help inform and guide future research and public health actions.

Keywords

• Interpretable machine learning
• Relative importance
• Lifestyle
• Overweight and obesity
• Physical activity
• Alcohol consumption