Identifying depression in the National Health and Nutrition Examination Survey data using a deep learning algorithm

• Published in: Journal of affective disorders 2019-10, Vol.257, p.623-631
• Main Authors: Oh, Jihoon, Yun, Kyongsik, Maoz, Uri, Kim, Tae-Suk, Chae, Jeong-Ho
• Format: Article
• Language: English
• Subjects: Deep learning; Depression; Machine learning; National Health and Nutrition Examination Survey
• ISSN: 0165-0327; 1573-2517
• DOI: 10.1016/j.jad.2019.06.034

•Estimating epidemiological contributors to depression and predicting the prevalence of depression are still challenging.•We aimed to estimate factors affecting depression in National Health and Nutrition Examination Survey (NHANES) datasets using deep learning and machine learning algorithms.•Deep-learning achieved a high performance for identifying depression on the NHANES datasets of both the United States and South Korea.•Trained deep-learning and machine learning algorithms are useful for estimating the prevalence of depression. As depression is the leading cause of disability worldwide, large-scale surveys have been conducted to establish the occurrence and risk factors of depression. However, accurately estimating epidemiological factors leading up to depression has remained challenging. Deep-learning algorithms can be applied to assess the factors leading up to prevalence and clinical manifestations of depression. Customized deep-neural-network and machine-learning classifiers were assessed using survey data from 19,725 participants from the NHANES database (from 1999 through 2014) and 4949 from the South Korea NHANES (K-NHANES) database in 2014. A deep-learning algorithm showed area under the receiver operating characteristic curve (AUCs) of 0.91 and 0.89 for detecting depression in NHANES and K-NHANES, respectively. The deep-learning algorithm trained with serial datasets (NHANES, from 1999 to 2012), predicted the prevalence of depression in the following two years of data (NHANES, 2013 and 2014) with an AUC of 0.92. Machine learning classifiers trained with NHANES could further predict depression in K-NHANES. There, logistic regression had the highest performance (AUC, 0.77) followed by deep learning algorithm (AUC, 0.74). Deep neural-networks managed to identify depression well from other health and demographic factors in both the NHANES and K-NHANES datasets. The deep-learning algorithm was also able to predict depression relatively well on new data set—cross temporally and cross nationally. Further research can delineate the clinical implications of machine learning and deep learning in detecting disease prevalence and progress as well as other risk factors for depression and other mental illnesses.