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Predicting long-term type 2 diabetes with support vector machine using oral glucose tolerance test
- Source :
- PLoS ONE, 14(12):e0219636. Public Library of Science, PLoS ONE, Vol 14, Iss 12, p e0219636 (2019), PLoS ONE
- Publication Year :
- 2019
- Publisher :
- Public Library of Science, 2019.
-
Abstract
- 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.
- Subjects :
- Blood Glucose
Male
Support Vector Machine
Physiology
Cardiovascular Medicine
Biochemistry
Body Mass Index
Machine Learning
Endocrinology
Mathematical and Statistical Techniques
Medicine and Health Sciences
Insulin
Statistics
Middle Aged
Body Fluids
Type 2 Diabetes
Blood
Cardiovascular Diseases
Physical Sciences
Medicine
Female
Anatomy
Research Article
Adult
Computer and Information Sciences
Endocrine Disorders
Science
Research and Analysis Methods
Blood Plasma
Artificial Intelligence
Diagnostic Medicine
Support Vector Machines
Diabetes Mellitus
Humans
Statistical Methods
Life Style
Diabetic Endocrinology
Biology and Life Sciences
Glucose Tolerance Test
Hormones
United States
Diabetes Mellitus, Type 2
Metabolic Disorders
Glucose Tolerance Tests
Insulin Resistance
Mathematics
Biomarkers
Forecasting
Subjects
Details
- Language :
- English
- ISSN :
- 19326203
- Database :
- OpenAIRE
- Journal :
- PLoS ONE, 14(12):e0219636. Public Library of Science, PLoS ONE, Vol 14, Iss 12, p e0219636 (2019), PLoS ONE
- Accession number :
- edsair.pmid.dedup....199f6c7872862e1a1aaf861d6bc887bb