Development and validation of an insulin resistance prediction model in children and adolescents using machine learning algorithms.

ABSTRACT:

Background

Insulin resistance (IR) is a precursor to metabolic disorders like type 2 diabetes and hypertension in children and adolescents. Early detection of IR is critical to prevent severe metabolic complications. IR is influenced by factors such as diet, inflammation, and genetics. However, existing studies often focus on limited populations and overlook dietary factors. This study aimed to evaluate the use of machine learning (ML) models for early IR prediction in children and adolescents, emphasizing accuracy.

Methods

We used physical examination data of children and adolescents aged 6-17 years from the China Health and Nutrition Survey (CHNS) database as the training set and collected routine physical examination data from children and adolescents aged 6-17 years admi

SUBMITTER: Huang X 

PROVIDER: S-EPMC11983009 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature