AI-driven prediction of insulin resistance in non-diabetic populations using minimal invasive tests: comparing models and criteria.

ABSTRACT:

Background

Insulin resistance is a key precursor to diabetes and increases the risk of cardiovascular diseases. Traditional assessment methods rely on multiple invasive tests. Developing an AI model based on minimally invasive tests, especially using only fasting blood glucose as the invasive test, can promote health monitoring in non-diabetic populations, particularly for frequent routine checks.

Objective

This study aims to develop an AI-driven model that uses only fasting blood glucose as the invasive measure to predict insulin resistance in non-diabetic populations. The goal is to facilitate health monitoring through simple, minimally invasive tests.

Methods

We selected simple and accessible input features, including age, gender, height, weight, pulse, blood press

SUBMITTER: Gao W 

PROVIDER: S-EPMC12359882 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature