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Machine learning for the prediction of atherosclerotic cardiovascular disease during 3‐year follow up in Chinese type 2 diabetes mellitus patients
AIMS/INTRODUCTION: Clinical guidelines for the management of individuals with type 2 diabetes mellitus endorse the systematic assessment of atherosclerotic cardiovascular disease risk for early interventions. In this study, we aimed to develop machine learning models to predict 3‐year atheroscleroti...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10583655/ https://www.ncbi.nlm.nih.gov/pubmed/37605871 http://dx.doi.org/10.1111/jdi.14069 |
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author | Ding, Jinru Luo, Yingying Shi, Huwei Chen, Ruiyao Luo, Shuqing Yang, Xu Xiao, Zhongzhou Liang, Bilin Yan, Qiujuan Xu, Jie Ji, Linong |
author_facet | Ding, Jinru Luo, Yingying Shi, Huwei Chen, Ruiyao Luo, Shuqing Yang, Xu Xiao, Zhongzhou Liang, Bilin Yan, Qiujuan Xu, Jie Ji, Linong |
author_sort | Ding, Jinru |
collection | PubMed |
description | AIMS/INTRODUCTION: Clinical guidelines for the management of individuals with type 2 diabetes mellitus endorse the systematic assessment of atherosclerotic cardiovascular disease risk for early interventions. In this study, we aimed to develop machine learning models to predict 3‐year atherosclerotic cardiovascular disease risk in Chinese type 2 diabetes mellitus patients. MATERIALS AND METHODS: Clinical records of 4,722 individuals with type 2 diabetes mellitus admitted to 94 hospitals were used. The features included demographic information, disease histories, laboratory tests and physical examinations. Logistic regression, support vector machine, gradient boosting decision tree, random forest and adaptive boosting were applied for model construction. The performance of these models was evaluated using the area under the receiver operating characteristic curve. Additionally, we applied SHapley Additive exPlanation values to explain the prediction model. RESULTS: All five models achieved good performance in both internal and external test sets (area under the receiver operating characteristic curve >0.8). Random forest showed the highest discrimination ability, with sensitivity and specificity being 0.838 and 0.814, respectively. The SHapley Additive exPlanation analyses showed that previous history of diabetic peripheral vascular disease, older populations and longer diabetes duration were the three most influential predictors. CONCLUSIONS: The prediction models offer opportunities to personalize treatment and maximize the benefits of these medical interventions. |
format | Online Article Text |
id | pubmed-10583655 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-105836552023-10-19 Machine learning for the prediction of atherosclerotic cardiovascular disease during 3‐year follow up in Chinese type 2 diabetes mellitus patients Ding, Jinru Luo, Yingying Shi, Huwei Chen, Ruiyao Luo, Shuqing Yang, Xu Xiao, Zhongzhou Liang, Bilin Yan, Qiujuan Xu, Jie Ji, Linong J Diabetes Investig Articles AIMS/INTRODUCTION: Clinical guidelines for the management of individuals with type 2 diabetes mellitus endorse the systematic assessment of atherosclerotic cardiovascular disease risk for early interventions. In this study, we aimed to develop machine learning models to predict 3‐year atherosclerotic cardiovascular disease risk in Chinese type 2 diabetes mellitus patients. MATERIALS AND METHODS: Clinical records of 4,722 individuals with type 2 diabetes mellitus admitted to 94 hospitals were used. The features included demographic information, disease histories, laboratory tests and physical examinations. Logistic regression, support vector machine, gradient boosting decision tree, random forest and adaptive boosting were applied for model construction. The performance of these models was evaluated using the area under the receiver operating characteristic curve. Additionally, we applied SHapley Additive exPlanation values to explain the prediction model. RESULTS: All five models achieved good performance in both internal and external test sets (area under the receiver operating characteristic curve >0.8). Random forest showed the highest discrimination ability, with sensitivity and specificity being 0.838 and 0.814, respectively. The SHapley Additive exPlanation analyses showed that previous history of diabetic peripheral vascular disease, older populations and longer diabetes duration were the three most influential predictors. CONCLUSIONS: The prediction models offer opportunities to personalize treatment and maximize the benefits of these medical interventions. John Wiley and Sons Inc. 2023-08-22 /pmc/articles/PMC10583655/ /pubmed/37605871 http://dx.doi.org/10.1111/jdi.14069 Text en © 2023 The Authors. Journal of Diabetes Investigation published by Asian Association for the Study of Diabetes (AASD) and John Wiley & Sons Australia, Ltd. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. |
spellingShingle | Articles Ding, Jinru Luo, Yingying Shi, Huwei Chen, Ruiyao Luo, Shuqing Yang, Xu Xiao, Zhongzhou Liang, Bilin Yan, Qiujuan Xu, Jie Ji, Linong Machine learning for the prediction of atherosclerotic cardiovascular disease during 3‐year follow up in Chinese type 2 diabetes mellitus patients |
title | Machine learning for the prediction of atherosclerotic cardiovascular disease during 3‐year follow up in Chinese type 2 diabetes mellitus patients |
title_full | Machine learning for the prediction of atherosclerotic cardiovascular disease during 3‐year follow up in Chinese type 2 diabetes mellitus patients |
title_fullStr | Machine learning for the prediction of atherosclerotic cardiovascular disease during 3‐year follow up in Chinese type 2 diabetes mellitus patients |
title_full_unstemmed | Machine learning for the prediction of atherosclerotic cardiovascular disease during 3‐year follow up in Chinese type 2 diabetes mellitus patients |
title_short | Machine learning for the prediction of atherosclerotic cardiovascular disease during 3‐year follow up in Chinese type 2 diabetes mellitus patients |
title_sort | machine learning for the prediction of atherosclerotic cardiovascular disease during 3‐year follow up in chinese type 2 diabetes mellitus patients |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10583655/ https://www.ncbi.nlm.nih.gov/pubmed/37605871 http://dx.doi.org/10.1111/jdi.14069 |
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