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The Forcing Factors That Predict Obesity: A Cross-Sectional Multilevel Machine Learning Model of US County-Level
Nicolaas P Pronk1,2,3, Shuaijie Wang3,4, Colin Woodard1,5
1HealthPartners Institute, Minneapolis, Minnesota, USA.
Objective:
Variables predicting obesity are not limited to individual-level risk factors. The purpose of this study is to assess multilevel predictors of obesity prevalence.
Methods:
US county-level datasets incorporating 34 variables were analyzed cross-sectionally using explainable artificial intelligence (XAI) analytical methods. A Light Gradient Boosting Machine Model was trained to predict obesity prevalence, after which model performance and feature importance were evaluated.
Results:
Optimal model performance included 29 features and explained 78% of the variance in county-level obesity prevalence. The dominant predictor of obesity prevalence was physical inactivity. Additional highly important variables include smoking, excessive drinking, political ideology, and regional culture.
Conclusions:
This study used XAI methods to predict obesity, explaining 78% of the variance at the granular county level. Inclusion of both upstream and downstream factors in multisectoral and multidisciplinary approaches to predicting population-level obesity prevalence is warranted.
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