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

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 ChinaCorrespondence: Zhengzhi...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Zhao M, Feng Z
Formato:	article
Lenguaje:	EN
Publicado:	Dove Medical Press 2020
Materias:	depression questionnaire military machine learning diagnosis. Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Neurology. Diseases of the nervous system RC346-429
Acceso en línea:	https://doaj.org/article/e784b07cad684d6dbfede761eaac6f00
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:e784b07cad684d6dbfede761eaac6f00
record_format	dspace
spelling	oai:doaj.org-article:e784b07cad684d6dbfede761eaac6f002021-12-02T12:58:08ZMachine Learning Methods to Evaluate the Depression Status of Chinese Recruits: A Diagnostic Study1178-2021https://doaj.org/article/e784b07cad684d6dbfede761eaac6f002020-11-01T00:00:00Zhttps://www.dovepress.com/machine-learning-methods-to-evaluate-the-depression-status-of-chinese--peer-reviewed-article-NDThttps://doaj.org/toc/1178-2021Mengxue 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 ChinaCorrespondence: Zhengzhi FengFaculty of Medical Psychology, Army Medical University, Chongqing, People’s Republic of ChinaTel +86 13228688828Fax +86 23-68752341Email fzz@tmmu.edu.cnPurpose: 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, diagnosisZhao MFeng ZDove Medical Pressarticledepressionquestionnairemilitarymachine learningdiagnosis.Neurosciences. Biological psychiatry. NeuropsychiatryRC321-571Neurology. Diseases of the nervous systemRC346-429ENNeuropsychiatric Disease and Treatment, Vol Volume 16, Pp 2743-2752 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	depression questionnaire military machine learning diagnosis. Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Neurology. Diseases of the nervous system RC346-429
spellingShingle	depression questionnaire military machine learning diagnosis. Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Neurology. Diseases of the nervous system RC346-429 Zhao M Feng Z Machine Learning Methods to Evaluate the Depression Status of Chinese Recruits: A Diagnostic Study
description	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 ChinaCorrespondence: Zhengzhi FengFaculty of Medical Psychology, Army Medical University, Chongqing, People’s Republic of ChinaTel +86 13228688828Fax +86 23-68752341Email fzz@tmmu.edu.cnPurpose: 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
format	article
author	Zhao M Feng Z
author_facet	Zhao M Feng Z
author_sort	Zhao M
title	Machine Learning Methods to Evaluate the Depression Status of Chinese Recruits: A Diagnostic Study
title_short	Machine Learning Methods to Evaluate the Depression Status of Chinese Recruits: A Diagnostic Study
title_full	Machine Learning Methods to Evaluate the Depression Status of Chinese Recruits: A Diagnostic Study
title_fullStr	Machine Learning Methods to Evaluate the Depression Status of Chinese Recruits: A Diagnostic Study
title_full_unstemmed	Machine Learning Methods to Evaluate the Depression Status of Chinese Recruits: A Diagnostic Study
title_sort	machine learning methods to evaluate the depression status of chinese recruits: a diagnostic study
publisher	Dove Medical Press
publishDate	2020
url	https://doaj.org/article/e784b07cad684d6dbfede761eaac6f00
work_keys_str_mv	AT zhaom machinelearningmethodstoevaluatethedepressionstatusofchineserecruitsadiagnosticstudy AT fengz machinelearningmethodstoevaluatethedepressionstatusofchineserecruitsadiagnosticstudy
_version_	1718393598768054272

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

Ejemplares similares