{Reference Type}: Journal Article
{Title}: Identifying depression in the United States veterans using deep learning algorithms, NHANES 2005-2018.
{Author}: Qu Z;Wang Y;Guo D;He G;Sui C;Duan Y;Zhang X;Lan L;Meng H;Wang Y;Liu X;
{Journal}: BMC Psychiatry
{Volume}: 23
{Issue}: 1
{Year}: 2023 08 23
{Factor}: 4.144
{DOI}: 10.1186/s12888-023-05109-9
{Abstract}: 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.<BR>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.<BR>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.<BR>Compared with traditional machine learning methods, deep learning algorithms achieved optimal performance, making it conducive for identifying depression and its risk factors among veterans.