Diabetes research and clinical practice · 2026
Predicting type II diabetes mellitus in young and middle-aged adults: A machine learning approach using the Utah population database.
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Meeks Huong D, Smith Ken R, Curtis David S, Date Ankita, Kowaleski-Jones Lori
Аннотация
AIMS: To develop a machine learning framework for predicting type 2 diabetes mellitus (T2DM) using administrative data and electronic health records (EHR) that could be applied in healthcare settings.
METHODS: Study population included parents of individuals born in 1970-1990 who resided in Utah urban counties during 1990-2015. Two prediction models were developed using classification and regression tree (CART) methods. A "follow-back" design used data from 2010-2015 to predict T2DM incidence between 2016-2021. An "age-based" design used data from ages 40-45 to predict T2DM incidence between ages 46-50. Potential predictors included individual sociodemographic characteristics, family history of T2DM, and neighborhood environmental measures.
RESULTS: The follow-back and age-based cohorts included 240,163 and 126,525 individuals, respectively. The final CART decision rules demonstrated high sensitivity (90-95%), with overweight status consistently selected as primary decision rule across study designs. Racial-ethnic minority populations and individuals living in urban/socioeconomically deprived areas were identified as having elevated risk for T2DM, even at younger ages and normal/underweight BMI.
CONCLUSIONS: Application of machine learning models for T2DM prediction should be tailored to specific study designs and population characteristics, with considerations of environmental data relevant to the local context. Opportunities exist to utilize administrative data and EHR for machine learning-based prediction.
METHODS: Study population included parents of individuals born in 1970-1990 who resided in Utah urban counties during 1990-2015. Two prediction models were developed using classification and regression tree (CART) methods. A "follow-back" design used data from 2010-2015 to predict T2DM incidence between 2016-2021. An "age-based" design used data from ages 40-45 to predict T2DM incidence between ages 46-50. Potential predictors included individual sociodemographic characteristics, family history of T2DM, and neighborhood environmental measures.
RESULTS: The follow-back and age-based cohorts included 240,163 and 126,525 individuals, respectively. The final CART decision rules demonstrated high sensitivity (90-95%), with overweight status consistently selected as primary decision rule across study designs. Racial-ethnic minority populations and individuals living in urban/socioeconomically deprived areas were identified as having elevated risk for T2DM, even at younger ages and normal/underweight BMI.
CONCLUSIONS: Application of machine learning models for T2DM prediction should be tailored to specific study designs and population characteristics, with considerations of environmental data relevant to the local context. Opportunities exist to utilize administrative data and EHR for machine learning-based prediction.