ArticlePloS one2023
An interpretable machine learning model of cross-sectional U.S. county-level obesity prevalence using explainable artificial intelligence.
Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 14 citations in OpenAlex.
- Machine Learning for the Analysis of Healthy Lifestyle Data: Scoping Review and Guidelines.JMIR human factors · 2026Guideline
- Identification of critical brain regions for young adults with obesity and their relationships with impulsivity using machine learning based on neuroanatomical features.International journal of obesity (2005) · 2026Article
- Comparative Evaluation of Explainable Machine Learning Versus Linear Regression for Predicting County-Level Lung Cancer Mortality Rate in the United States.JCO clinical cancer informatics · 2025Article
- Social and Behavioral Factors Associated With Diabetes in Southern California vs the US.JAMA network open · 2025Article
- Explainable Machine Learning for the Early Clinical Detection of Ovarian Cancer Using Contrastive Explanations.Journal of clinical medicine · 2025Article
- Interpretable Prediction of Myocardial Infarction Using Explainable Boosting Machines: A Biomarker-Based Machine Learning Approach.Diagnostics (Basel, Switzerland) · 2025Article
- Bridging the gap in obesity research: A consensus statement from the European Society for Clinical Investigation.European journal of clinical investigation · 2025Review
- Spatiotemporal prediction of obesity rates and model interpretability analysis from a public health perspective.PloS one · 2025Article
- Machine-learning-based model for analysing and accurately predicting factors related to burnout in healthcare workers.BMJ public health · 2025Article
- 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.BMC public health · 2024Article
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Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThere 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.
objectiveThe goal of this study is to extract knowledge from machine learning models for county-level variation in obesity prevalence.
methodsThis 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.
resultsThe 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.
conclusionsInterpretable machine learning models of health behaviors and outcomes provide substantial insight into obesity prevalence variation across counties in the United States.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.