Machine Learning-Based Prediction of Depressive Disorders via Various Data Modalities: A Survey

Qiong Li; Xiaotong Liu; Xuecai Hu; Md Atiqur Rahman Ahad; Min Ren; Li Yao; Yongzhen Huang

doi:10.1109/JAS.2025.125393

IEEE/CAA Journal of Automatica Sinica

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Q. Li, X. Liu, X. Hu, M. Ahad, M. Ren, L. Yao, and Y. Huang, “Machine learning-based prediction of depressive disorders via various data modalities: A survey,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 7, pp. 1320–1349, Jul. 2025. doi: 10.1109/JAS.2025.125393

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Q. Li, X. Liu, X. Hu, M. Ahad, M. Ren, L. Yao, and Y. Huang, “Machine learning-based prediction of depressive disorders via various data modalities: A survey,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 7, pp. 1320–1349, Jul. 2025. doi: 10.1109/JAS.2025.125393

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Machine Learning-Based Prediction of Depressive Disorders via Various Data Modalities: A Survey

doi: 10.1109/JAS.2025.125393

Funds: This work was supported by the National Natural Science Foundation of China (62276025, 62206022), the Shenzhen Technology Plan Program (KQTD20170331093217368), and the China Postdoctoral Science Foundation (BX20230044, 2023M730290)

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Author Bio:
Qiong Li received the B.E. degree from Shandong Normal University in 2017, and her M.S. degree from Guangxi University in 2020. She is currently pursuing the Ph.D. degree in computer application technology with Bejing Normal University. Her research interests include pattern recognition, computer vision, and machine learning

Xiaotong Liu received the B.E. degree from Chongqing University of Posts and Telecommunications in 2017, and the M.S. degree from Chongqing University of Posts and Telecommunications in 2020. She is currently pursuing the Ph.D. degree in computer application technology with Bejing Normal University. Her research interests include pattern recognition, computer vision, and machine learning

Xuecai Hu received the B.E. degree from University of Science and Technology of China in 2015, where he is currently pursuing the Ph.D. degree. His research interests include computer vision, image enhancement, and semantic segmentation

Md Atiqur Rahman Ahad is an Associate Professor of Artificial Intelligence and Machine Learning (Champion, Research and Innovation) Department of Computer Science and Digital Technologies, University of East London, UK. He became a Professor at the University of Dhaka in 2018 and served as a specially appointed Associate Professor at Osaka University. He works on computer vision, imaging, IoT, etc. He was awarded the UGC Gold Medal (handed by the Honorable President of Bangladesh), JSPS Postdoctoral Fellowship, and 40 awards/scholarships

Min Ren received the B.E. degree in mechanical engineering and automation from the National University of Defense Technology in 2013, and the Ph.D. degree in pattern recognition and intelligent systems from the Institute of Automation, Chinese Academy of Sciences (CASIA) in 2023. He is currently a postdoctoral fellow at the School of Artificial Intelligence, Beijing Normal University. His research interests include pattern recognition and biometrics

Li Yao received the B.S. degree from Beijing Normal University, in 1983, and the Ph.D. degree from Chinese Academy of Science in 1998. She is currently a Professor of the School of Artificial Intelligence, Beijing Normal University. Her research interests include brain signal processing, and neuroimaging data analysis

Yongzhen Huang received the B.E. degree from Huazhong University of Science and Technology in 2006, and the Ph.D. degree from Institute of Automation, Chinese Academy of Sciences in 2011. He is currently a Professor at the School of Artificial Intelligence, Beijing Normal University. He has published one book and more than 80 papers at international journals and conferences such as TPAMI, IJCV, TIP, TSMCB, TMM, TCSVT, CVPR, ICCV, ECCV, NIPS, AAAI. His research interests include pattern recognition, computer vision, and machine learning
Corresponding author: Min Ren, e-mail: renmin@bnu.edu.cn; Yongzhen Huang, e-mail: huangyongzhen@bnu.edu.cn
Qiong Li and Xiaotong Liu contributed equally to this work.
Received Date: 2024-09-27
Revised Date: 2024-12-06
Accepted Date: 2025-03-02

Available Online: 2025-05-26

Abstract

Abstract

Depression, a pervasive mental health disorder, has substantial impacts on both individuals and society. The conventional approach to predicting depression necessitates substantial collaboration between health care professionals and patients, leaving room for the influence of subjective factors. Consequently, it is imperative to develop a more efficient and accessible prediction methodology for depression. In recent years, numerous investigations have delved into depression prediction techniques, employing diverse data modalities and yielding notable advancements. Given the rapid progression of this domain, the present article comprehensively reviews major breakthroughs in depression prediction, encompassing multiple data modalities such as electrophysiological signals, brain imaging, audiovisual data, and text. By integrating depression prediction methods from various data modalities, it offers a comparative assessment of their advantages and limitations, providing a well-rounded perspective on how different modalities can complement each other for more accurate and holistic depression prediction. The survey begins by examining commonly used datasets, evaluation metrics, and methodological frameworks. For each data modality, it systematically analyzes traditional machine learning methods alongside the increasingly prevalent deep learning approaches, providing a comparative assessment of detection frameworks, feature representations, context modeling, and training strategies. Finally, the survey culminates with the identification of prospective avenues that warrant further exploration. It provides researchers with valuable insights and practical guidance to advance the field of depression prediction.
- Audiovisual data,
- brain imaging,
- depression prediction,
- electrophysiological signals,
- machine learning,
- text

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Qiong Li and Xiaotong Liu contributed equally to this work.

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Machine Learning-Based Prediction of Depressive Disorders via Various Data Modalities: A Survey

doi: 10.1109/JAS.2025.125393

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