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

Authors Zhao M, Feng Z

Received 5 August 2020

Accepted for publication 19 October 2020

Published 12 November 2020 Volume 2020:16 Pages 2743—2752

DOI https://doi.org/10.2147/NDT.S275620

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Dr Yuping Ning


Mengxue Zhao,1 Zhengzhi Feng2

1Department of Military Psychology, Faculty of Medical Psychology, Army Medical University, Chongqing, People’s Republic of China; 2Faculty of Medical Psychology, Army Medical University, Chongqing, People’s Republic of China

Correspondence: Zhengzhi Feng
Faculty of Medical Psychology, Army Medical University, Chongqing, People’s Republic of China
Tel +86 13228688828
Fax +86 23-68752341
Email fzz@tmmu.edu.cn

Purpose: 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.
Patients and Methods: 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).
Results: 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.
Conclusion: 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.

Keywords: depression, questionnaire, military, machine learning, diagnosis

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