Staff View: An intelligent mental health identification method for college students: A mixed-method study

An intelligent mental health identification method for college students: A mixed-method study

Purpose: Mental health assessments that combine patients’ facial expressions and behaviors have been proven effective, but screening large-scale student populations for mental health problems is time-consuming and labor-intensive. This study aims to provide an efficient and accurate intelligent meth...

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Main Authors:	Li, Chong, Yang, Mingzhao, Zhang, Yongting, Lai, Khin Wee
Format:	Article
Published:	MDPI 2022
Subjects:	BF Psychology TA Engineering (General). Civil engineering (General)
Online Access:	http://eprints.um.edu.my/40606/
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spelling	my.um.eprints.406062023-10-17T16:40:00Z http://eprints.um.edu.my/40606/ An intelligent mental health identification method for college students: A mixed-method study Li, Chong Yang, Mingzhao Zhang, Yongting Lai, Khin Wee BF Psychology TA Engineering (General). Civil engineering (General) Purpose: Mental health assessments that combine patients’ facial expressions and behaviors have been proven effective, but screening large-scale student populations for mental health problems is time-consuming and labor-intensive. This study aims to provide an efficient and accurate intelligent method for further psychological diagnosis and treatment, which combines artificial intelligence technologies to assist in evaluating the mental health problems of college students. Materials and Methods: We propose a mixed-method study of mental health assessment that combines psychological questionnaires with facial emotion analysis to comprehensively evaluate the mental health of students on a large scale. The Depression Anxiety and Stress Scale-21(DASS-21) is used for the psychological questionnaire. The facial emotion recognition model is implemented by transfer learning based on neural networks, and the model is pre-trained using FER2013 and CFEE datasets. Among them, the FER2013 dataset consists of 48 × 48-pixel face gray images, a total of 35,887 face images. The CFEE dataset contains 950,000 facial images with annotated action units (au). Using a random sampling strategy, we sent online questionnaires to 400 college students and received 374 responses, and the response rate was 93.5%. After pre-processing, 350 results were available, including 187 male and 153 female students. First, the facial emotion data of students were collected in an online questionnaire test. Then, a pre-trained model was used for emotion recognition. Finally, the online psychological questionnaire scores and the facial emotion recognition model scores were collated to give a comprehensive psychological evaluation score. Results: The experimental results of the facial emotion recognition model proposed to show that its classification results are broadly consistent with the mental health survey results. This model can be used to improve efficiency. In particular, the accuracy of the facial emotion recognition model proposed in this paper is higher than that of the general mental health model, which only uses the traditional single questionnaire. Furthermore, the absolute errors of this study in the three symptoms of depression, anxiety, and stress are lower than other mental health survey results and are only 0.8%, 8.1%, 3.5%, and 1.8%, respectively. Conclusion: The mixed method combining intelligent methods and scales for mental health assessment has high recognition accuracy. Therefore, it can support efficient large-scale screening of students’ psychological problems. © 2022 by the authors. MDPI 2022-11 Article PeerReviewed Li, Chong and Yang, Mingzhao and Zhang, Yongting and Lai, Khin Wee (2022) An intelligent mental health identification method for college students: A mixed-method study. International Journal of Environmental Research and Public Health, 19 (22). ISSN 1660-4601, DOI https://doi.org/10.3390/ijerph192214976 <https://doi.org/10.3390/ijerph192214976>. 10.3390/ijerph192214976
institution	Universiti Malaya
building	UM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaya
content_source	UM Research Repository
url_provider	http://eprints.um.edu.my/
topic	BF Psychology TA Engineering (General). Civil engineering (General)
spellingShingle	BF Psychology TA Engineering (General). Civil engineering (General) Li, Chong Yang, Mingzhao Zhang, Yongting Lai, Khin Wee An intelligent mental health identification method for college students: A mixed-method study
description	Purpose: Mental health assessments that combine patients’ facial expressions and behaviors have been proven effective, but screening large-scale student populations for mental health problems is time-consuming and labor-intensive. This study aims to provide an efficient and accurate intelligent method for further psychological diagnosis and treatment, which combines artificial intelligence technologies to assist in evaluating the mental health problems of college students. Materials and Methods: We propose a mixed-method study of mental health assessment that combines psychological questionnaires with facial emotion analysis to comprehensively evaluate the mental health of students on a large scale. The Depression Anxiety and Stress Scale-21(DASS-21) is used for the psychological questionnaire. The facial emotion recognition model is implemented by transfer learning based on neural networks, and the model is pre-trained using FER2013 and CFEE datasets. Among them, the FER2013 dataset consists of 48 × 48-pixel face gray images, a total of 35,887 face images. The CFEE dataset contains 950,000 facial images with annotated action units (au). Using a random sampling strategy, we sent online questionnaires to 400 college students and received 374 responses, and the response rate was 93.5%. After pre-processing, 350 results were available, including 187 male and 153 female students. First, the facial emotion data of students were collected in an online questionnaire test. Then, a pre-trained model was used for emotion recognition. Finally, the online psychological questionnaire scores and the facial emotion recognition model scores were collated to give a comprehensive psychological evaluation score. Results: The experimental results of the facial emotion recognition model proposed to show that its classification results are broadly consistent with the mental health survey results. This model can be used to improve efficiency. In particular, the accuracy of the facial emotion recognition model proposed in this paper is higher than that of the general mental health model, which only uses the traditional single questionnaire. Furthermore, the absolute errors of this study in the three symptoms of depression, anxiety, and stress are lower than other mental health survey results and are only 0.8%, 8.1%, 3.5%, and 1.8%, respectively. Conclusion: The mixed method combining intelligent methods and scales for mental health assessment has high recognition accuracy. Therefore, it can support efficient large-scale screening of students’ psychological problems. © 2022 by the authors.
format	Article
author	Li, Chong Yang, Mingzhao Zhang, Yongting Lai, Khin Wee
author_facet	Li, Chong Yang, Mingzhao Zhang, Yongting Lai, Khin Wee
author_sort	Li, Chong
title	An intelligent mental health identification method for college students: A mixed-method study
title_short	An intelligent mental health identification method for college students: A mixed-method study
title_full	An intelligent mental health identification method for college students: A mixed-method study
title_fullStr	An intelligent mental health identification method for college students: A mixed-method study
title_full_unstemmed	An intelligent mental health identification method for college students: A mixed-method study
title_sort	intelligent mental health identification method for college students: a mixed-method study
publisher	MDPI
publishDate	2022
url	http://eprints.um.edu.my/40606/
_version_	1781704521794191360
score	13.211869

An intelligent mental health identification method for college students: A mixed-method study

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