Abstract

The focus of this paper is to examine factors associated with carotid atherosclerosis in patients with impaired glucose tolerance (IGT), and to predict the rapid progression of carotid intima-media thickness (IMT). The proposed machine learning methods performed well and accurately predicted the progression of carotid IMT. The linear support vector machine, nonlinear support vector machine with a radial basis kernel function, multilayer perceptron (MLP), and the Naive Bayes method were employed. A comparison of these methods was conducted using the Brier score, and the accuracy was tested using a confusion matrix.

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