Developing a Simple Non-Laboratory-Based Machine Learning Tool for Prediabetes Screening in a Target Population: A Proof-of-Concept Study.

ABSTRACT:

Background

Progression from prediabetes to type 2 diabetes (T2D) can be delayed with early detection and intervention. Current detection methods, relying on costly blood glucose tests, limit widespread screening. Machine learning models offer the potential for non-laboratory-based tools. However, existing prediabetes detection models lack validation in their intended target populations. Thus, this study aimed to develop and validate a non-laboratory-based machine learning tool for prediabetes detection in a specific target population.

Methods

Based on 501 adults from a prediabetes screening project, a decision tree model was developed. Twelve potential non-laboratory-based features were extracted. The target variable was categorized into prediabetes (hemoglobin A1c [HbA

SUBMITTER: Holm TF 

PROVIDER: S-EPMC12460283 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature