Predicting long-term type 2 diabetes with support vector machine using oral glucose tolerance test
<p dir="ltr">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 t...
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2019
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| _version_ | 1864513520305963008 |
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| author | Hasan T. Abbas (8115014) |
| author2 | Lejla Alic (207667) Madhav Erraguntla (4919803) Jim X. Ji (4669933) Muhammad Abdul-Ghani (4707436) Qammer H. Abbasi (8115017) Marwa K. Qaraqe (8115020) |
| author2_role | author author author author author author |
| author_facet | Hasan T. Abbas (8115014) Lejla Alic (207667) Madhav Erraguntla (4919803) Jim X. Ji (4669933) Muhammad Abdul-Ghani (4707436) Qammer H. Abbasi (8115017) Marwa K. Qaraqe (8115020) |
| author_role | author |
| dc.creator.none.fl_str_mv | Hasan T. Abbas (8115014) Lejla Alic (207667) Madhav Erraguntla (4919803) Jim X. Ji (4669933) Muhammad Abdul-Ghani (4707436) Qammer H. Abbasi (8115017) Marwa K. Qaraqe (8115020) |
| dc.date.none.fl_str_mv | 2019-12-11T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0219636 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Predicting_long-term_type_2_diabetes_with_support_vector_machine_using_oral_glucose_tolerance_test/25428268 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biomedical and clinical sciences Clinical sciences Glucose Diabetes mellitus Insulin Blood plasma Forecasting Type 2 diabetes Support vector machines Type 2 diabetes risk |
| dc.title.none.fl_str_mv | Predicting long-term type 2 diabetes with support vector machine using oral glucose tolerance test |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: PLOS ONE<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1371/journal.pone.0219636" target="_blank">https://dx.doi.org/10.1371/journal.pone.0219636</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_e57d82f2edabf0916127ff2c7599aefc |
| identifier_str_mv | 10.1371/journal.pone.0219636 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25428268 |
| publishDate | 2019 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Predicting long-term type 2 diabetes with support vector machine using oral glucose tolerance testHasan T. Abbas (8115014)Lejla Alic (207667)Madhav Erraguntla (4919803)Jim X. Ji (4669933)Muhammad Abdul-Ghani (4707436)Qammer H. Abbasi (8115017)Marwa K. Qaraqe (8115020)Biomedical and clinical sciencesClinical sciencesGlucoseDiabetes mellitusInsulinBlood plasmaForecastingType 2 diabetesSupport vector machinesType 2 diabetes risk<p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: PLOS ONE<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1371/journal.pone.0219636" target="_blank">https://dx.doi.org/10.1371/journal.pone.0219636</a></p>2019-12-11T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1371/journal.pone.0219636https://figshare.com/articles/journal_contribution/Predicting_long-term_type_2_diabetes_with_support_vector_machine_using_oral_glucose_tolerance_test/25428268CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/254282682019-12-11T03:00:00Z |
| spellingShingle | Predicting long-term type 2 diabetes with support vector machine using oral glucose tolerance test Hasan T. Abbas (8115014) Biomedical and clinical sciences Clinical sciences Glucose Diabetes mellitus Insulin Blood plasma Forecasting Type 2 diabetes Support vector machines Type 2 diabetes risk |
| status_str | publishedVersion |
| 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 | Biomedical and clinical sciences Clinical sciences Glucose Diabetes mellitus Insulin Blood plasma Forecasting Type 2 diabetes Support vector machines Type 2 diabetes risk |