The Forcing Factors That Predict Obesity: A Cross-Sectional Multilevel Machine Learning Model of US County-Level Prevalence.
cross_sectional · Level IV
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- Record sourced from PubMed, PMID 42663397.
- Also identified by DOI 10.1002/oby.70283.
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Abstract
Variables predicting obesity are not limited to individual-level risk factors. The purpose of this study is to assess multilevel predictors of obesity prevalence. 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. 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. 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.