RT Journal Article
SR Electronic
T1 Predicting long-term Type 2 Diabetes with Support Vector Machine using Oral Glucose Tolerance Test
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 688804
DO 10.1101/688804
A1 Hasan Abbas
A1 Lejla Alic
A1 Madhav Erraguntla
A1 Jim Ji
A1 Muhammad Abdul-Ghani
A1 Qammer Abbasi
A1 Marwa Qaraqe
YR 2019
UL http://biorxiv.org/content/early/2019/07/01/688804.abstract
AB 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 dataset. Using 11 oral glucose tolerance test (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.