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

被引:18
|
作者
Chu, Haiyun [1 ]
Chen, Lu [2 ]
Yang, Xiuxian [1 ]
Qiu, Xiaohui [1 ]
Qiao, Zhengxue [1 ]
Song, Xuejia [1 ]
Zhao, Erying [1 ]
Zhou, Jiawei [1 ]
Zhang, Wenxin [1 ]
Mehmood, Anam [1 ]
Pan, Hui [2 ]
Yang, Yanjie [1 ]
机构
[1] Harbin Med Univ, Dept Med Psychol, Harbin, Peoples R China
[2] Peking Union Med Coll Hosp, Dept Endocrinol, Beijing, Peoples R China
来源
FRONTIERS IN PSYCHOLOGY | 2021年 / 12卷
关键词
type 2 diabetes mellitus; cardiovascular disease; bio-psycho-social factors; machine learning; China; ECONOMIC BURDEN; RISK; CHOLESTEROL; SYMPTOMS; ADULTS; COMPLICATIONS; EPIDEMIOLOGY; ASSOCIATIONS; METAANALYSIS; DYSFUNCTION;
D O I
10.3389/fpsyg.2021.645418
中图分类号
B84 [心理学];
学科分类号
04 ; 0402 ;
摘要
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.
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收藏
页数:8
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