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Prognostic Modeling and Prevention of Diabetes Using Machine Learning Technique
Stratifying individuals at risk for developing diabetes could enable targeted delivery of interventional programs to those at highest risk, while avoiding the effort and costs of prevention and treatment in those at low risk. The objective of this study was to explore the potential role of a Hidden...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6760163/ https://www.ncbi.nlm.nih.gov/pubmed/31551457 http://dx.doi.org/10.1038/s41598-019-49563-6 |
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author | Perveen, Sajida Shahbaz, Muhammad Keshavjee, Karim Guergachi, Aziz |
author_facet | Perveen, Sajida Shahbaz, Muhammad Keshavjee, Karim Guergachi, Aziz |
author_sort | Perveen, Sajida |
collection | PubMed |
description | Stratifying individuals at risk for developing diabetes could enable targeted delivery of interventional programs to those at highest risk, while avoiding the effort and costs of prevention and treatment in those at low risk. The objective of this study was to explore the potential role of a Hidden Markov Model (HMM), a machine learning technique, in validating the performance of the Framingham Diabetes Risk Scoring Model (FDRSM), a well-respected prognostic model. Can HMM predict 8-year risk of developing diabetes in an individual effectively? To our knowledge, no study has attempted use of HMM to validate the performance of FDRSM. We used Electronic Medical Record (EMR) data, of 172,168 primary care patients to derive the 8-year risk of developing diabetes in an individual using HMM. The Area Under Receiver Operating Characteristic Curve (AROC) in our study sample of 911 individuals for whom all risk factors and follow up data were available is 86.9% compared to AROCs of 78.6% and 85% reported in a previously conducted validation study of FDRSM in the same Canadian population and the Framingham study respectively. These results demonstrate that the discrimination capability of our proposed HMM is superior to the validation study conducted using the FDRSM in a Canadian population and in the Framingham population. We conclude that HMM is capable of identifying patients at increased risk of developing diabetes within the next 8-years. |
format | Online Article Text |
id | pubmed-6760163 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-67601632019-11-12 Prognostic Modeling and Prevention of Diabetes Using Machine Learning Technique Perveen, Sajida Shahbaz, Muhammad Keshavjee, Karim Guergachi, Aziz Sci Rep Article Stratifying individuals at risk for developing diabetes could enable targeted delivery of interventional programs to those at highest risk, while avoiding the effort and costs of prevention and treatment in those at low risk. The objective of this study was to explore the potential role of a Hidden Markov Model (HMM), a machine learning technique, in validating the performance of the Framingham Diabetes Risk Scoring Model (FDRSM), a well-respected prognostic model. Can HMM predict 8-year risk of developing diabetes in an individual effectively? To our knowledge, no study has attempted use of HMM to validate the performance of FDRSM. We used Electronic Medical Record (EMR) data, of 172,168 primary care patients to derive the 8-year risk of developing diabetes in an individual using HMM. The Area Under Receiver Operating Characteristic Curve (AROC) in our study sample of 911 individuals for whom all risk factors and follow up data were available is 86.9% compared to AROCs of 78.6% and 85% reported in a previously conducted validation study of FDRSM in the same Canadian population and the Framingham study respectively. These results demonstrate that the discrimination capability of our proposed HMM is superior to the validation study conducted using the FDRSM in a Canadian population and in the Framingham population. We conclude that HMM is capable of identifying patients at increased risk of developing diabetes within the next 8-years. Nature Publishing Group UK 2019-09-24 /pmc/articles/PMC6760163/ /pubmed/31551457 http://dx.doi.org/10.1038/s41598-019-49563-6 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Perveen, Sajida Shahbaz, Muhammad Keshavjee, Karim Guergachi, Aziz Prognostic Modeling and Prevention of Diabetes Using Machine Learning Technique |
title | Prognostic Modeling and Prevention of Diabetes Using Machine Learning Technique |
title_full | Prognostic Modeling and Prevention of Diabetes Using Machine Learning Technique |
title_fullStr | Prognostic Modeling and Prevention of Diabetes Using Machine Learning Technique |
title_full_unstemmed | Prognostic Modeling and Prevention of Diabetes Using Machine Learning Technique |
title_short | Prognostic Modeling and Prevention of Diabetes Using Machine Learning Technique |
title_sort | prognostic modeling and prevention of diabetes using machine learning technique |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6760163/ https://www.ncbi.nlm.nih.gov/pubmed/31551457 http://dx.doi.org/10.1038/s41598-019-49563-6 |
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