ArticleScientific reports2021
Geographically weighted machine learning model for untangling spatial heterogeneity of type 2 diabetes mellitus (T2D) prevalence in the USA.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Spatial and spatiotemporal machine learning models for COVID-19 dynamics: a review of methodology and reporting practices.Epidemiologic reviews · 2025Pooled it
- Built Environment, Community Safety, and Diabetes in the Deep South: A County Level Geospatial Analysis.Journal of racial and ethnic health disparities · 2026Article
- Multidimensional drivers of diabetes prevalence among middle-aged and elderly in China: from a geographical perspective.Diabetology & metabolic syndrome · 2026Article
- The intersection of place and health: examining the association of social determinants of health and diabetes in children and adolescents in Florida.BMC public health · 2026Article
- Spatial analysis of county-level determinants of overdose mortality in the United States using spatial machine learning.BMC public health · 2025Article
- The generative revolution: AI foundation models in geospatial health-applications, challenges and future research.International journal of health geographics · 2025Article
- Analysis of the Predictors of Mortality from Ischemic Heart Diseases in the Southern Region of Brazil: A Geographic Machine-Learning-Based Study.Global heart · 2024Article
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- An early warning model of type 2 diabetes risk based on POI visit history and food access management.PloS one · 2023Article
- Spatial patterns in sociodemographic factors explain to a large extent the prevalence of hypertension and diabetes in Aragon (Spain).Frontiers in medicine · 2023Article
- Variation in and Factors Associated With US County-Level Cancer Mortality, 2008-2019.JAMA network open · 2022Article
- Ranking the importance of demographic, socioeconomic, and underlying health factors on US COVID-19 deaths: A geographical random forest approach.Health & place · 2022Article
- Alzheimer's Disease Dementia Prevalence in the United States: A County-Level Spatial Machine Learning Analysis.American journal of Alzheimer's disease and other dementiasArticle
Corrections and comments
- Erratum issued
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Type 2 diabetes mellitus (T2D) prevalence in the United States varies substantially across spatial and temporal scales, attributable to variations of socioeconomic and lifestyle risk factors. Understanding these variations in risk factors contributions to T2D would be of great benefit to intervention and treatment approaches to reduce or prevent T2D. Geographically-weighted random forest (GW-RF), a tree-based non-parametric machine learning model, may help explore and visualize the relationships between T2D and risk factors at the county-level. GW-RF outputs are compared to global (RF and OLS) and local (GW-OLS) models between the years of 2013-2017 using low education, poverty, obesity, physical inactivity, access to exercise, and food environment as inputs. Our results indicate that a non-parametric GW-RF model shows a high potential for explaining spatial heterogeneity of, and predicting, T2D prevalence over traditional local and global models when inputting six major risk factors. Some of these predictions, however, are marginal. These findings of spatial heterogeneity using GW-RF demonstrate the need to consider local factors in prevention approaches. Spatial analysis of T2D and associated risk factor prevalence offers useful information for targeting the geographic area for prevention and disease interventions.
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