Identifying depression in the United States veterans using deep learning algorithms, NHANES 2005–2018

Background Depression is a common mental health problem among veterans, with high mortality. Despite the numerous conducted investigations, the prediction and identification of risk factors for depression are still severely limited. This study used a deep learning algorithm to identify depression in...

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Published in:BMC psychiatry Vol. 23; no. 1; pp. 1 - 10
Main Authors: Qu, Zihan, Wang, Yashan, Guo, Dingjie, He, Guangliang, Sui, Chuanying, Duan, Yuqing, Zhang, Xin, Lan, Linwei, Meng, Hengyu, Wang, Yajing, Liu, Xin
Format: Journal Article
Language:English
Published: London BioMed Central 23.08.2023
BioMed Central Ltd
Springer Nature B.V
BMC
Subjects:
Accuracy
Age
Algorithms
Armed forces
Blood pressure
Cardiovascular disease
Care and treatment
Chronic diseases
Chronic illnesses
Classification
Cohort analysis
Deep learning
Depression
Depression, Mental
Diagnosis
Health aspects
Hypertension
Learning algorithms
Machine learning
Medicine
Medicine & Public Health
Mental depression
Methods
NHANES
Palmitic acid
Personal health
Population
Prevention
Psychiatry
Psychological aspects
Psychotherapy
Risk factors
Support vector machines
Variables
Veterans
Vitamins
United States
United States > US
Deep learning
Depression
NHANES
Veterans
Machine learning
ISSN:1471-244X, 1471-244X
Online Access:Get full text
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Summary:Background Depression is a common mental health problem among veterans, with high mortality. Despite the numerous conducted investigations, the prediction and identification of risk factors for depression are still severely limited. This study used a deep learning algorithm to identify depression in veterans and its factors associated with clinical manifestations. Methods Our data originated from the National Health and Nutrition Examination Survey (2005–2018). A dataset of 2,546 veterans was identified using deep learning and five traditional machine learning algorithms with 10-fold cross-validation. Model performance was assessed by examining the area under the subject operating characteristic curve (AUC), accuracy, recall, specificity, precision, and F1 score. Results Deep learning had the highest AUC (0.891, 95%CI 0.869–0.914) and specificity (0.906) in identifying depression in veterans. Further study on depression among veterans of different ages showed that the AUC values for deep learning were 0.929 (95%CI 0.904–0.955) in the middle-aged group and 0.924(95%CI 0.900-0.948) in the older age group. In addition to general health conditions, sleep difficulties, memory impairment, work incapacity, income, BMI, and chronic diseases, factors such as vitamins E and C, and palmitic acid were also identified as important influencing factors. Conclusions Compared with traditional machine learning methods, deep learning algorithms achieved optimal performance, making it conducive for identifying depression and its risk factors among veterans.
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ISSN:1471-244X
1471-244X
DOI:10.1186/s12888-023-05109-9