An innovative model for predicting coronary heart disease using triglyceride-glucose index: a machine learning-based cohort study.

ABSTRACT:

Background

Various predictive models have been developed for predicting the incidence of coronary heart disease (CHD), but none of them has had optimal predictive value. Although these models consider diabetes as an important CHD risk factor, they do not consider insulin resistance or triglyceride (TG). The unsatisfactory performance of these prediction models may be attributed to the ignoring of these factors despite their proven effects on CHD. We decided to modify standard CHD predictive models through machine learning to determine whether the triglyceride-glucose index (TyG-index, a logarithmized combination of fasting blood sugar (FBS) and TG that demonstrates insulin resistance) functions better than diabetes as a CHD predictor.

Methods

Two-thousand participants of a c

SUBMITTER: Mirjalili SR 

PROVIDER: S-EPMC10403891 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature