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Prediction of carotid atherosclerosis in patients with impaired glucose tolerance - a performance analysis of machine learning techniques

Author

Listed:
  • A. Maruthamuthu
  • Murugesan Punniyamoorthy
  • Swetha Manasa Paluru
  • Sindhura Tammuluri

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.

Suggested Citation

  • A. Maruthamuthu & Murugesan Punniyamoorthy & Swetha Manasa Paluru & Sindhura Tammuluri, 2019. "Prediction of carotid atherosclerosis in patients with impaired glucose tolerance - a performance analysis of machine learning techniques," International Journal of Enterprise Network Management, Inderscience Enterprises Ltd, vol. 10(2), pages 109-117.
    • Handle: RePEc:ids:ijenma:v:10:y:2019:i:2:p:109-117
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