Cargando…

Roles of Anxiety and Depression in Predicting Cardiovascular Disease Among Patients With Type 2 Diabetes Mellitus: A Machine Learning Approach

Cardiovascular disease (CVD) is a major complication of type 2 diabetes mellitus (T2DM). In addition to traditional risk factors, psychological determinants play an important role in CVD risk. This study applied Deep Neural Network (DNN) to develop a CVD risk prediction model and explored the bio-ps...

Descripción completa

Detalles Bibliográficos
Autores principales: Chu, Haiyun, Chen, Lu, Yang, Xiuxian, Qiu, Xiaohui, Qiao, Zhengxue, Song, Xuejia, Zhao, Erying, Zhou, Jiawei, Zhang, Wenxin, Mehmood, Anam, Pan, Hui, Yang, Yanjie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8113686/
https://www.ncbi.nlm.nih.gov/pubmed/33995200
http://dx.doi.org/10.3389/fpsyg.2021.645418
_version_ 1783690914823143424
author Chu, Haiyun
Chen, Lu
Yang, Xiuxian
Qiu, Xiaohui
Qiao, Zhengxue
Song, Xuejia
Zhao, Erying
Zhou, Jiawei
Zhang, Wenxin
Mehmood, Anam
Pan, Hui
Yang, Yanjie
author_facet Chu, Haiyun
Chen, Lu
Yang, Xiuxian
Qiu, Xiaohui
Qiao, Zhengxue
Song, Xuejia
Zhao, Erying
Zhou, Jiawei
Zhang, Wenxin
Mehmood, Anam
Pan, Hui
Yang, Yanjie
author_sort Chu, Haiyun
collection PubMed
description Cardiovascular disease (CVD) is a major complication of type 2 diabetes mellitus (T2DM). In addition to traditional risk factors, psychological determinants play an important role in CVD risk. This study applied Deep Neural Network (DNN) to develop a CVD risk prediction model and explored the bio-psycho-social contributors to the CVD risk among patients with T2DM. From 2017 to 2020, 834 patients with T2DM were recruited from the Department of Endocrinology, Affiliated Hospital of Harbin Medical University, China. In this cross-sectional study, the patients' bio-psycho-social information was collected through clinical examinations and questionnaires. The dataset was randomly split into a 75% train set and a 25% test set. DNN was implemented at the best performance on the train set and applied on the test set. The receiver operating characteristic curve (ROC) analysis was used to evaluate the model performance. Of participants, 272 (32.6%) were diagnosed with CVD. The developed ensemble model for CVD risk achieved an area under curve score of 0.91, accuracy of 87.50%, sensitivity of 88.06%, and specificity of 87.23%. Among patients with T2DM, the top five predictors in the CVD risk model were body mass index, anxiety, depression, total cholesterol, and systolic blood pressure. In summary, machine learning models can provide an automated identification mechanism for patients at CVD risk. Integrated treatment measures should be taken in health management, including clinical care, mental health improvement, and health behavior promotion.
format Online
Article
Text
id pubmed-8113686
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-81136862021-05-13 Roles of Anxiety and Depression in Predicting Cardiovascular Disease Among Patients With Type 2 Diabetes Mellitus: A Machine Learning Approach Chu, Haiyun Chen, Lu Yang, Xiuxian Qiu, Xiaohui Qiao, Zhengxue Song, Xuejia Zhao, Erying Zhou, Jiawei Zhang, Wenxin Mehmood, Anam Pan, Hui Yang, Yanjie Front Psychol Psychology Cardiovascular disease (CVD) is a major complication of type 2 diabetes mellitus (T2DM). In addition to traditional risk factors, psychological determinants play an important role in CVD risk. This study applied Deep Neural Network (DNN) to develop a CVD risk prediction model and explored the bio-psycho-social contributors to the CVD risk among patients with T2DM. From 2017 to 2020, 834 patients with T2DM were recruited from the Department of Endocrinology, Affiliated Hospital of Harbin Medical University, China. In this cross-sectional study, the patients' bio-psycho-social information was collected through clinical examinations and questionnaires. The dataset was randomly split into a 75% train set and a 25% test set. DNN was implemented at the best performance on the train set and applied on the test set. The receiver operating characteristic curve (ROC) analysis was used to evaluate the model performance. Of participants, 272 (32.6%) were diagnosed with CVD. The developed ensemble model for CVD risk achieved an area under curve score of 0.91, accuracy of 87.50%, sensitivity of 88.06%, and specificity of 87.23%. Among patients with T2DM, the top five predictors in the CVD risk model were body mass index, anxiety, depression, total cholesterol, and systolic blood pressure. In summary, machine learning models can provide an automated identification mechanism for patients at CVD risk. Integrated treatment measures should be taken in health management, including clinical care, mental health improvement, and health behavior promotion. Frontiers Media S.A. 2021-04-28 /pmc/articles/PMC8113686/ /pubmed/33995200 http://dx.doi.org/10.3389/fpsyg.2021.645418 Text en Copyright © 2021 Chu, Chen, Yang, Qiu, Qiao, Song, Zhao, Zhou, Zhang, Mehmood, Pan and Yang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Psychology
Chu, Haiyun
Chen, Lu
Yang, Xiuxian
Qiu, Xiaohui
Qiao, Zhengxue
Song, Xuejia
Zhao, Erying
Zhou, Jiawei
Zhang, Wenxin
Mehmood, Anam
Pan, Hui
Yang, Yanjie
Roles of Anxiety and Depression in Predicting Cardiovascular Disease Among Patients With Type 2 Diabetes Mellitus: A Machine Learning Approach
title Roles of Anxiety and Depression in Predicting Cardiovascular Disease Among Patients With Type 2 Diabetes Mellitus: A Machine Learning Approach
title_full Roles of Anxiety and Depression in Predicting Cardiovascular Disease Among Patients With Type 2 Diabetes Mellitus: A Machine Learning Approach
title_fullStr Roles of Anxiety and Depression in Predicting Cardiovascular Disease Among Patients With Type 2 Diabetes Mellitus: A Machine Learning Approach
title_full_unstemmed Roles of Anxiety and Depression in Predicting Cardiovascular Disease Among Patients With Type 2 Diabetes Mellitus: A Machine Learning Approach
title_short Roles of Anxiety and Depression in Predicting Cardiovascular Disease Among Patients With Type 2 Diabetes Mellitus: A Machine Learning Approach
title_sort roles of anxiety and depression in predicting cardiovascular disease among patients with type 2 diabetes mellitus: a machine learning approach
topic Psychology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8113686/
https://www.ncbi.nlm.nih.gov/pubmed/33995200
http://dx.doi.org/10.3389/fpsyg.2021.645418
work_keys_str_mv AT chuhaiyun rolesofanxietyanddepressioninpredictingcardiovasculardiseaseamongpatientswithtype2diabetesmellitusamachinelearningapproach
AT chenlu rolesofanxietyanddepressioninpredictingcardiovasculardiseaseamongpatientswithtype2diabetesmellitusamachinelearningapproach
AT yangxiuxian rolesofanxietyanddepressioninpredictingcardiovasculardiseaseamongpatientswithtype2diabetesmellitusamachinelearningapproach
AT qiuxiaohui rolesofanxietyanddepressioninpredictingcardiovasculardiseaseamongpatientswithtype2diabetesmellitusamachinelearningapproach
AT qiaozhengxue rolesofanxietyanddepressioninpredictingcardiovasculardiseaseamongpatientswithtype2diabetesmellitusamachinelearningapproach
AT songxuejia rolesofanxietyanddepressioninpredictingcardiovasculardiseaseamongpatientswithtype2diabetesmellitusamachinelearningapproach
AT zhaoerying rolesofanxietyanddepressioninpredictingcardiovasculardiseaseamongpatientswithtype2diabetesmellitusamachinelearningapproach
AT zhoujiawei rolesofanxietyanddepressioninpredictingcardiovasculardiseaseamongpatientswithtype2diabetesmellitusamachinelearningapproach
AT zhangwenxin rolesofanxietyanddepressioninpredictingcardiovasculardiseaseamongpatientswithtype2diabetesmellitusamachinelearningapproach
AT mehmoodanam rolesofanxietyanddepressioninpredictingcardiovasculardiseaseamongpatientswithtype2diabetesmellitusamachinelearningapproach
AT panhui rolesofanxietyanddepressioninpredictingcardiovasculardiseaseamongpatientswithtype2diabetesmellitusamachinelearningapproach
AT yangyanjie rolesofanxietyanddepressioninpredictingcardiovasculardiseaseamongpatientswithtype2diabetesmellitusamachinelearningapproach