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Identifying depression in the United States veterans using deep learning algorithms, NHANES 2005–2018.
- Source :
-
BMC Psychiatry . 8/23/2023, Vol. 23 Issue 1, p1-10. 10p. 1 Diagram, 4 Charts, 2 Graphs. - Publication Year :
- 2023
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Abstract
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 1471244X
- Volume :
- 23
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- BMC Psychiatry
- Publication Type :
- Academic Journal
- Accession number :
- 170080931
- Full Text :
- https://doi.org/10.1186/s12888-023-05109-9