ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2026
Machine Learning Based County Level Phenotypes Related to Diabetes Prevalence.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 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
In the US, diabetes prevalence rates continue to rise. However little focus is given on the association of diabetes with the social determinants of health (SDoH). This study focuses on developing phenotypes based on county-level SDoH which are related to diabetes prevalence. Machine learning algorithms such as classification and regression tree (CART) model were used to define phenotypes based on county-level SDoH. Random forest was also used to identify additional risk factors. Five different phenotypes identified by the CART model divided the counties into five groups. Counties with high food insecurity rates (more than 16%) and high poverty rates (more than 24%) were found to have higher mean prevalence rate of diabetes (17.64%, SD 2.42). These can help individuals involved in healthcare and policy makers to make tailored region-based interventions to reduce diabetes prevalence and improve living conditions for people with diabetes.
Identifiers
42317806PMC13274274What OpenQuestion holds
Registered trials
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