Ruan, Z., Yang, P., Huang, J. et al. (3 more authors) (2024) Automatic Depression Detection Among Higher Education Students Based on DeepFM. IEEE Transactions on Instrumentation and Measurement, 73. 2521010. ISSN 0018-9456
Abstract
Depressive disorder has become a common problem among higher education students, but it often gets undiagnosed and untreated due to unrecognized symptoms, poor access to medical resources, and fear of stigma. To improve the situation, automatic depression detection would be essential. In this paper, we explore the feasibility of depression detection in higher education students using their behavioral data automatically collected by the University system. First, a DeepFM network, which can not only take discrete-continuous mixed features as its input but also can learn linear and nonlinear relations between the input and the output, is presented for depression detection. A modified focal loss function (MFL) is then proposed to alleviate data imbalance impact caused by the fact that the proportion of healthy students outweighs those diagnosed with depression significantly. To verify the effectiveness of the proposed method, behavioral data from 3218 students were collected, of which 179 were diagnosed with depression by university psychologists using PHQ-9 scale scores. 5-fold cross-validations are performed, and the experiment results have illustrated that DeepFM obtains the highest average accuracy compared to Multilayer Perceptron (MLP), Factorisation Neural Network (FNN), and Product-based Neural Network (PNN), demonstrating the effectiveness of the proposed framework for depression detection among university students.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | This is an author produced version of an article published in IEEE Transactions on Instrumentation and Measurement, made available under the terms of the Creative Commons Attribution License (CC-BY), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. |
Keywords: | Depression detection, data imbalance, deep learning, DeepFM, campus big data |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Robotics, Autonomous Systems & Sensing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 09 Feb 2024 11:03 |
Last Modified: | 07 Aug 2024 12:47 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/TIM.2024.3413175 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:208968 |