An interpretable machine learning model of cross-sectional U.S. county-level obesity prevalence using explainable artificial intelligence
![Thumbnail](/bitstream/handle/1808/35096/pone.0292341.pdf.jpg?sequence=4&isAllowed=y)
View/ Open
Issue Date
2023-10-05Author
Allen, Ben
Publisher
PLOS ONE
Type
Article
Article Version
Scholarly/refereed, publisher version
Rights
Copyright © 2023 Ben Allen
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Metadata
Show full item recordAbstract
Background
There is considerable geographic heterogeneity in obesity prevalence across counties in the United States. Machine learning algorithms accurately predict geographic variation in obesity prevalence, but the models are often uninterpretable and viewed as a black-box.Objective
The goal of this study is to extract knowledge from machine learning models for county-level variation in obesity prevalence.Methods
This study shows the application of explainable artificial intelligence methods to machine learning models of cross-sectional obesity prevalence data collected from 3,142 counties in the United States. County-level features from 7 broad categories: health outcomes, health behaviors, clinical care, social and economic factors, physical environment, demographics, and severe housing conditions. Explainable methods applied to random forest prediction models include feature importance, accumulated local effects, global surrogate decision tree, and local interpretable model-agnostic explanations.Results
The results show that machine learning models explained 79% of the variance in obesity prevalence, with physical inactivity, diabetes, and smoking prevalence being the most important factors in predicting obesity prevalence.Conclusions
Interpretable machine learning models of health behaviors and outcomes provide substantial insight into obesity prevalence variation across counties in the United States.
Collections
Citation
Allen B. An interpretable machine learning model of cross-sectional U.S. county-level obesity prevalence using explainable artificial intelligence. PLoS One. 2023 Oct 5;18(10):e0292341. doi: 10.1371/journal.pone.0292341. PMID: 37796874; PMCID: PMC10553328
Items in KU ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
We want to hear from you! Please share your stories about how Open Access to this item benefits YOU.