Use of Automated Machine Learning to Detect Undiagnosed Diabetes in US Adults: Development and Validation Study.

ABSTRACT:

Background

Early diagnosis of diabetes is essential for early interventions to slow the progression of dysglycemia and its comorbidities. However, among individuals with diabetes, about 23% were unaware of their condition.

Objective

This study aims to investigate the potential use of automated machine learning (AutoML) models and self-reported data in detecting undiagnosed diabetes among US adults.

Methods

Individual-level data, including biochemical tests for diabetes, demographic characteristics, family history of diabetes, anthropometric measures, dietary intakes, health behaviors, and chronic conditions, were retrieved from the National Health and Nutrition Examination Survey, 1999-2020. Undiagnosed diabetes was defined as having no prior self-reported diagnosis b

SUBMITTER: Liu J 

PROVIDER: S-EPMC12532270 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature