ArticleAmerican journal of medicine open2026
Machine Learning Models to Evaluate County-Level Incidence of Diagnosed Diabetes and Sociodemographic Factors.
Article in American journal of medicine open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Aims: To evaluate county-level incidence of diagnosed diabetes and key sociodemographic factors in a high-dimensional, nonlinear setting. Methods: This temporally aggregated observational study used US Centers for Disease Control and Prevention data on county-level incidence of diagnosed diabetes, from 2004 to 2019, and 34 sociodemographic factors from public databases. We defined counties as Results: Overall, 500 of 3114 counties (16.1%) were of higher-burden. Elastic net regression showed good predictive performance for estimating diabetes incidence ( Conclusions: Machine learning models demonstrated consistent performance in estimating and classifying county-level diabetes incidence, with high discrimination for identifying higher-burden counties. Sociodemographic factors, including
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