Cargando…
Predicting long-term type 2 diabetes with support vector machine using oral glucose tolerance test
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 auto...
Autores principales: | , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6905529/ https://www.ncbi.nlm.nih.gov/pubmed/31826018 http://dx.doi.org/10.1371/journal.pone.0219636 |
_version_ | 1783478178667298816 |
---|---|
author | Abbas, Hasan T. Alic, Lejla Erraguntla, Madhav Ji, Jim X. Abdul-Ghani, Muhammad Abbasi, Qammer H. Qaraqe, Marwa K. |
author_facet | Abbas, Hasan T. Alic, Lejla Erraguntla, Madhav Ji, Jim X. Abdul-Ghani, Muhammad Abbasi, Qammer H. Qaraqe, Marwa K. |
author_sort | Abbas, Hasan T. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6905529 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-69055292019-12-27 Predicting long-term type 2 diabetes with support vector machine using oral glucose tolerance test Abbas, Hasan T. Alic, Lejla Erraguntla, Madhav Ji, Jim X. Abdul-Ghani, Muhammad Abbasi, Qammer H. Qaraqe, Marwa K. PLoS One Research Article 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. Public Library of Science 2019-12-11 /pmc/articles/PMC6905529/ /pubmed/31826018 http://dx.doi.org/10.1371/journal.pone.0219636 Text en © 2019 Abbas et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Abbas, Hasan T. Alic, Lejla Erraguntla, Madhav Ji, Jim X. Abdul-Ghani, Muhammad Abbasi, Qammer H. Qaraqe, Marwa K. Predicting long-term type 2 diabetes with support vector machine using oral glucose tolerance test |
title | Predicting long-term type 2 diabetes with support vector machine using oral glucose tolerance test |
title_full | Predicting long-term type 2 diabetes with support vector machine using oral glucose tolerance test |
title_fullStr | Predicting long-term type 2 diabetes with support vector machine using oral glucose tolerance test |
title_full_unstemmed | Predicting long-term type 2 diabetes with support vector machine using oral glucose tolerance test |
title_short | Predicting long-term type 2 diabetes with support vector machine using oral glucose tolerance test |
title_sort | predicting long-term type 2 diabetes with support vector machine using oral glucose tolerance test |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6905529/ https://www.ncbi.nlm.nih.gov/pubmed/31826018 http://dx.doi.org/10.1371/journal.pone.0219636 |
work_keys_str_mv | AT abbashasant predictinglongtermtype2diabeteswithsupportvectormachineusingoralglucosetolerancetest AT aliclejla predictinglongtermtype2diabeteswithsupportvectormachineusingoralglucosetolerancetest AT erraguntlamadhav predictinglongtermtype2diabeteswithsupportvectormachineusingoralglucosetolerancetest AT jijimx predictinglongtermtype2diabeteswithsupportvectormachineusingoralglucosetolerancetest AT abdulghanimuhammad predictinglongtermtype2diabeteswithsupportvectormachineusingoralglucosetolerancetest AT abbasiqammerh predictinglongtermtype2diabeteswithsupportvectormachineusingoralglucosetolerancetest AT qaraqemarwak predictinglongtermtype2diabeteswithsupportvectormachineusingoralglucosetolerancetest |