Depression, among the widely common psychological ailments across the globe, has a great influence on the
psychological well-being of an individual concerning their mood, cognitive functions, and behavioral aspects. Lack of
awareness and minimal availability of psychological professionals often create challenges in detecting depression at an earlier
stage. From the past few years, the use of Machine Learning and Artificial Intelligence has been identified as a potential way
towards detecting depression using behavior pattern identification.
It outlines a number of behavioural analysis-based techniques for diagnosing depression, such as those that use social media
messaging, speech patterns, EEG signals, and physiological traits. Numerous models based on machine and deep learning
approaches, including ensemble algorithms, SVM, KNN, Artificial Neural Networks, and combination models, are discussed
in significant research.
Along with optimisation techniques like GA and particle swarm optimisation, feature optimisation and its significance are
also identified. The current work compares the models, limitations, and data sets created by earlier research studies. The
importance and future necessity of an integrated data approach and expert feature selection in the successful detection of
depression are finally highlighted by the discussion.
This study aims to provide readers with an organized understanding of earlier advancements in intelligent mental health
monitoring systems, while also laying the groundwork for future research.
[1] K. A. L. Govindasamy and N. Palanichamy, ―Depression detection using machine learning techniques on Twitter data,‖
Proc. IEEE, 2021, Part Number: CFP21K74-ART, ISBN: 978-0-7381-1327-2.
[2] Md. S. Zulfiker, N. Kabir, A. A. Biswas, T. Nazneen, and M. S. Uddin, ―An in-depth analysis of machine learning
approaches to predict depression,‖ Current Research in Behavioral Sciences, vol. 2, p. 100044, 2021. doi:
10.1016/j.crbeha.2021.100044.
[3] A. A. Mirza, M. Baig, G. M. Beyari, M. A. Halawani, and A. R. Mirza, ―Depression and anxiety among medical students:
A brief overview,‖ Journal Name, vol. 12, pp. 393–398, 2021.
[4] R. Kushwaha, ―Depression detection on social media,‖ in Proc. ICI 2022, 2022. doi: 10.1109/ICI53355.2022.9786922.
[5] R. K. Bhoge, S. A. Nagare, S. P. Mahajan, and P. S. Kor, ―Depression detection by analyzing social media posts of users,‖
Int. J. Res. Appl. Sci. Eng. Technol., vol. 10, no. IV, Apr. 2022. [Online]. Available: www.ijraset.com
[6] S. Dabhane and P. M. Chawan, ―Depression detection on social media using machine learning techniques,‖ Int. J.
Compute. Appl., vol. 9, no. 4, 2021.
[7] T. Vankayala, K. Sathya Babu, and B. Sahoo, ―Depression detection from social media text analysis using natural
language processing techniques and hybrid deep learning model,‖ ACM Trans. Asian Low-Resour. Lang. Inf. Process., vol.
23, no. 1, pp. 1–20, 2024.
[8] L. S. Khoo, M. K. Lim, C. Y. Chong, and R. McNaney, ―Machine learning for multimodal mental health detection: A
systematic review of passive sensing approaches,‖ Sensors, vol. 24, no. 2, p. 348, 2024.
[9] H. S. AlSagri and M. Ykhlef, ―Machine learning-based approach for depression detection in Twitter using content and
activity features,‖ IEICE Trans. Inf. Syst., vol. 103, no. 8, pp. 1825–1832, 2020. doi: 10.1587/TRANSINF.2020EDP7023.
[10] P. Rakesh and Y. M. Riyaz Uddin, ―Ensemble hybrid learning methods for automated depression detection,‖ Int. J. Sci.
Eng. Res., vol. 13, no. 4, Apr. 2024. ISSN 2456–5083.
[11] U. M. Haque, E. Kabir, and R. Khanam, ―Detection of child depression using machine learning methods,‖ PLOS ONE,
2021. doi: 10.1371/journal.pone.0261131.
[12] M. Patil, P. Mukherji, and V. Wadhai, ―Biomedical signal processing and control,‖ Sci. Direct, 2023.
[13] S. Bhadra and C. J. Kumar, ―Enhancing the efficacy of depression detection systems using optimal feature selection from
EHR,‖ Compute. Methods Biotech. Biomed. Engin., vol. 27, no. 2, pp. 222–236, 2023.
[14] T. M. Martins et al., ―A machine learning approach to immobility detection in mice during the tail suspension test for
depressive-type behavior analysis,‖ Res. Biomed. Eng., vol. 39, no. 1, pp. 15–26, 2023.
[15] Uyulan, ―Depression diagnosis modeling with advanced computational methods: Frequency-domain eMVAR and deep
learning,‖ Clin. EEG Neurosis., vol. 53, no. 1, pp. 24–36, 2022.
[16] D. Suganthi and Geetha, ―Predicting postpartum depression with aid of social media texts using an optimized machine
learning model,‖ J. Inell. Syst., vol. 17, no. 3, 2024.
[17] J. Cha, S. Kim, and E. Park, ―A lexicon-based approach to examine depression detection in social media: The case of
Twitter and university community,‖ Humanit. Soc. Sci. Commun., vol. 9, no. 1, 2022. doi: 10.1057/s41599-022-01313-2.
[18] SUBHADIPMAITI-DEV, ―Depression Detection System Using Machine Learning,‖ GitHub. [Online].
Available:https://github.com/SUBHADIPMAITI-DEV/Depression-Detection-System-Using
MachineLearning/tree/main/Datasets
[19] S. Almutairi et l., ―A Hybrid Deep Learning Model for Predicting Depression Symptoms from Large-Scale Textual
Dataset,‖ IEEE Access, vol. 12, pp. 8477–8485, 2024. doi: 10.1109/ACCESS.2024.349674.
