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

Abstract

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.

Machine-learning prediction of BMI change among doctors and nurses in North China during the COVID-19 pandemic

“…Indeed, this scale was used in many Chinese researches and showed good reliability and validity (57, 58). DNN is typically used to make a prediction or classification through a series of layers, each of which combines an affine operation and a non-linearity (41). The DNN consists of an input layer, an output layer, and several hidden layers.…”

“…Deep Neural Network (DNN) is typically used to make a prediction through a series of layers, each of which combines an affine operation and a non-linearity. Studies showed that DNN models could provide an automated identification mechanism for various diseases, such as cardiovascular disease ( 41 ), diabetic retinopathy ( 42 ), neurological disorders ( 43 ), etc. Hence, in order to provide insights, augment prevention, and reduce risks in weight management among healthcare workers, there are great benefits of applying machine learning technology to develop a prediction model of BMI change among doctors and nurses during the COVID-19 pandemic.…”

Prediction of suicidal ideation among Chinese college students based on radial basis function neural network

“…G-mean is often used to evaluate the effect of prediction classification model in unbalanced data ( 53 ). An AUC of 1.0 represents a perfect test, with no false positive rate and no false negative rate, while an AUC of 0.5 indicates that the test performed no better than chance ( 54 ). Moreover, we used mean impact value (MIV) to identify important predictors for suicidal ideation ( 55 ).…”

Anxiety Levels Predict Bone Mineral Density in Postmenopausal Women Undergoing Oral Bisphosphonates: A Two-Year Follow-Up

“…There is growing interest regarding the crucial role of clinical psychological factors in chronic conditions [1][2][3][4][5]. It is well known that psychological features may help predict medical diseases, referring to both personality traits and mood states (e.g., alexithymia, depression, anxiety) as well as conscious and unconscious strategies (e.g., coping, defense mechanisms) [6][7][8][9][10][11][12][13][14]. On one hand, chronic conditions and their linked outcomes could lead to psychological symptoms, compromising the patients' health-related quality of life (HR-QoL) [15,16].…”

“…Due to several existing therapies [39][40][41][42], which have demonstrated specific efficacy in the treatment of osteoporosis by reducing fracture risk, it could be interesting to investigate, in postmenopausal women, the potential role of psychological features in determining and influencing the osteoporosis treatment response. As well, among several psychological features, anxiety seems to represent a meaningful vulnerability factor, since in chronic conditions individuals fear for the progressive loss of bodily intactness and are faced with managing the unexpected [2,6,8,13]. The aim of this study was to longitudinally investigate the association between anxiety levels and both adherence and treatment response to oral bisphosphonates (BPs).…”