Automatic Depression Detection Among Higher Education Students Based on DeepFM

• Published in: IEEE transactions on instrumentation and measurement Vol. 73; pp. 1 - 10
• Main Authors: Ruan, Ziling, Yang, Pengfei, Huang, Jiayang, Yang, Keyi, Lv, Yidan, Zhang, Zhi-Qiang
• Format: Journal Article
• Language: English
• Published: New York IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Campus big data; Colleges & universities; data imbalance; Data models; deep learning; DeepFM; Depression; depression detection; Education; Effectiveness; Electroencephalography; Feature extraction; Frequency modulation; Higher education; Mental depression; Multilayer perceptrons; Neural networks; Social networking (online); Students; University students; Vectors
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2024.3413175

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 article, 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 (MFL) function 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. Fivefold cross validations are performed, and the experiment results have illustrated that DeepFM obtains the highest average accuracy compared with multilayer perceptron (MLP), factorization neural network (FNN), and product-based neural network (PNN), demonstrating the effectiveness of the proposed framework for depression detection among university students.