Machine learning to predict rapid progression of carotid atherosclerosis in patients with impaired glucose tolerance.

ABSTRACT:

Objectives

Prediabetes is a major epidemic and is associated with adverse cardio-cerebrovascular outcomes. Early identification of patients who will develop rapid progression of atherosclerosis could be beneficial for improved risk stratification. In this paper, we investigate important factors impacting the prediction, using several machine learning methods, of rapid progression of carotid intima-media thickness in impaired glucose tolerance (IGT) participants.

Methods

In the Actos Now for Prevention of Diabetes (ACT NOW) study, 382 participants with IGT underwent carotid intima-media thickness (CIMT) ultrasound evaluation at baseline and at 15-18 months, and were divided into rapid progressors (RP, n = 39, 58 ± 17.5 μM change) and non-rapid progressors (NRP, n

SUBMITTER: Hu X 

PROVIDER: S-EPMC5011483 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature