Predicting long-term type 2 diabetes with support vector machine using oral glucose tolerance test

Abbas, H. T. , Alic, L., Erraguntla, M., Ji, J. X., Abdul-Ghani, M., Abbasi, Q. H. and Qaraqe, M. K. (2019) Predicting long-term type 2 diabetes with support vector machine using oral glucose tolerance test. PLoS ONE, 14(12), e0219636. (doi: 10.1371/journal.pone.0219636) (PMID:31826018) (PMCID:PMC6905529)

203111.pdf - Published Version
Available under License Creative Commons Attribution.



Diabetes is a large healthcare burden worldwide. There is substantial evidence that lifestyle modifications and drug intervention can prevent diabetes, therefore, an early identification of high risk individuals is important to design targeted prevention strategies. In this paper, we present an automatic tool that uses machine learning techniques to predict the development of type 2 diabetes mellitus (T2DM). Data generated from an oral glucose tolerance test (OGTT) was used to develop a predictive model based on the support vector machine (SVM). We trained and validated the models using the OGTT and demographic data of 1,492 healthy individuals collected during the San Antonio Heart Study. This study collected plasma glucose and insulin concentrations before glucose intake and at three time-points thereafter (30, 60 and 120 min). Furthermore, personal information such as age, ethnicity and body-mass index was also a part of the data-set. Using 11 OGTT measurements, we have deduced 61 features, which are then assigned a rank and the top ten features are shortlisted using minimum redundancy maximum relevance feature selection algorithm. All possible combinations of the 10 best ranked features were used to generate SVM based prediction models. This research shows that an individual’s plasma glucose levels, and the information derived therefrom have the strongest predictive performance for the future development of T2DM. Significantly, insulin and demographic features do not provide additional performance improvement for diabetes prediction. The results of this work identify the parsimonious clinical data needed to be collected for an efficient prediction of T2DM. Our approach shows an average accuracy of 96.80% and a sensitivity of 80.09% obtained on a holdout set.

Item Type:Articles
Additional Information:Funding: This publication was made possible by NPRP grant number NPRP 10-1231-160071 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. The publication of this article was funded by the Qatar National Library.
Glasgow Author(s) Enlighten ID:Abbas, Dr Hasan and Abbasi, Dr Qammer
Creator Roles:
Abbas, H. T.Formal analysis, Methodology, Software, Writing – original draft, Writing – review and editing
Abbasi, Q. H.Supervision, Validation, Writing – review and editing
Authors: Abbas, H. T., Alic, L., Erraguntla, M., Ji, J. X., Abdul-Ghani, M., Abbasi, Q. H., and Qaraqe, M. K.
College/School:College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
Journal Name:PLoS ONE
Publisher:Public Library of Science
ISSN (Online):1932-6203
Copyright Holders:Copyright © 2019 Abbas et al.
First Published:First published in PLoS ONE 14(12): e0219636
Publisher Policy:Reproduced under a Creative Commons license

University Staff: Request a correction | Enlighten Editors: Update this record