Machine Learning Methods to Evaluate the Depression Status of Chinese Recruits: A Diagnostic Study

Traditional questionnaires assessing the severity of depression are limited and might not be appropriate for military personnel. We intend to explore the diagnostic ability of three machine learning methods for evaluating the depression status of Chinese recruits, using the Chinese version of Beck Depression Inventory-II (BDI-II) as the standard.

Our diagnostic study was carried out in Luoyang City (Henan Province, China; 10/16/2018-12/10/2018) with a sample of 1000 Chinese male recruits selected using cluster convenient sampling. All participants completed the BDI and 3 questionnaires including the data of demographics, military careers and 18 factors. The participants were randomly selected as the training set and the testing at 2:1. The machine learning methods tested for assessing the presence or absence of depression status were neural network (NN), support vector machine (SVM), and decision tree (DT).

A total of 1000 participants completed the questionnaires, with 223 reporting depression status and 777 not. The highest sensitivity was observed for DT (94.1%), followed by SVM (93.4%) and NN (93.1%). The highest specificity was observed for NN (60.0%), followed by SVM (58.8%) and DT (43.3%). The area under the curve (AUC) of the SVM was the largest (0.862) compared with NN (0.860) and DT (0.734). The regression prediction error and error volatility of the SVM were the smallest.

The SVM has the smallest prediction error and error volatility, as well as the largest AUC compared with NN and DT for assessing the presence or absence of depression status in Chinese recruits.