Development and Validation of Machine Learning Models for Identifying Prediabetes and Diabetes in Normoglycemia.

ABSTRACT:

Background

Prediabetes and diabetes are both abnormal states of glucose metabolism (AGM) that can lead to severe complications. Early detection of AGM is crucial for timely intervention and treatment. However, fasting blood glucose (FBG) as a mass population screening method may fail to identify some individuals who are actually AGM but with normoglycemia. This study aimed to develop and validate machine learning (ML) models to identify AGM among individuals with normoglycemia using routine health check-up indicators.

Methods

According to the American Diabetes Association (ADA) criteria, participants with normoglycemia (FBG ≤ 5.6 mmol/L) were collected from 2019 to 2023, and then divided into AGM and Normal groups using glycosylated haemoglobin (HbA1c) 5.7% as the threshold.

SUBMITTER: Zhang X 

PROVIDER: S-EPMC11601146 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature