Machine learning approaches to mental stress detection: a review

Document Type : Original Article


Department of Computer Science, School of Computing, DIT University, Dehradun, 248001 India


Purpose of Review: Machine Learning has shown exponential growth in ingesting a huge
amount of data and give accurate outcomes equivalent to the human level. It provides a
glance at the future where complex data, analysis and analytical model together help
innumerable people suffering from health issues. This paper reviews the current application
of ML in the health sector, their limitation, predictive analysis, and areas that are hard-to-diagnose and need advance research.
New Findings: We have reviewed 30 papers on mental stress detection using ML that used
Social networking sites, student’s record, Questioner technique, clinical dataset, real-time data, Bio-signal technology, wireless device and suicidal tendency. Collectively, these studies show high accuracy and potential of ML algorithms in mental health, and which ML algorithm yields the best result.
Summary: With the advancement of ML, it has unfolded many areas like traditional clinical
trials which are not sufficient to collect all the information about a person. Currently, define
under DSM-V stage to detect these illnesses at the preliminary stage, diagnosing and treating
before any mishap. It has re-defined the mental health practicing reducing cost and time,
making it easier and convenient for patients to reach better health care whenever they need it.


H. Yazdavar, M. S. Mahdavinejad, G. Bajaj, K. Thirunarayan, J. Pathak and A. Sheth, "Mental Health Analysis Via Social Media Data," 2018 IEEE International Conference on Healthcare Informatics (ICHI), New York, NY, USA, 2018, pp. 459-460, doi: 10.1109/ICHI.2018.00102
Ghaderi, J. Frounchi and A. Farnam, "Machine learning-based signal processing using physiological signals for stress detection," 2015 22nd Iranian Conference on Biomedical Engineering (ICBME), Tehran, Iran, 2015, pp. 93-98, doi: 10.1109/ICBME.2015.7404123.
R. Subhani, W. Mumtaz, M. N. B. M. Saad, N. Kamel and A. S. Malik, "Machine Learning Framework for the Detection of Mental Stress at Multiple Levels," in IEEE Access, vol. 5, pp. 13545-13556, 2017, doi: 10.1109/ACCESS.2017.2723622.
Ahuja, Ravinder & Banga, Alisha. (2019). Mental Stress Detection in University Students using Machine Learning Algorithms. Procedia Computer Science. 152. 349-353. 10.1016/j.procs.2019.05.007.
Troussas, M. Virvou, K. J. Espinosa, K. Llaguno and J. Caro, "Sentiment analysis of Facebook statuses using Naive Bayes classifier for language learning," IISA 2013, Piraeus, Greece, 2013, pp. 1-6, doi: 10.1109/IISA.2013.6623713.
Vuppalapati, M. S. khan, N. Raghu, P. Veluru and S. Khursheed, "A System To Detect Mental Stress Using Machine Learning And Mobile Development," 2018 International Conference on Machine Learning and Cybernetics (ICMLC), Chengdu, China, 2018, pp. 161- 166, doi: 10.1109/ICMLC.2018.8527004.
Cavazos-Rehg PA, Krauss MJ, Sowles S, Connolly S, Rosas C, Bharadwaj M, Bierut LJ. A content analysis of depression-related Tweets. Comput Human Behav. 2016 Jan 1;54:351- 357. doi: 10.1016/j.chb.2015.08.023. PMID: 26392678; PMCID: PMC4574287.
Chancellor, S., De Choudhury, M. Methods in predictive techniques for mental health status on social media: a critical review. npj Digit. Med. 3, 43 (2020).
Cho G, Yim J, Choi Y, Ko J, Lee SH. Review of Machine Learning Algorithms for Diagnosing Mental Illness. Psychiatry Investig. 2019 Apr;16(4):262-269. doi: 10.30773/pi.2018.12.21.2. Epub 2019 Apr 8. PMID: 30947496; PMCID: PMC6504772.
Devakunchari Ramalingam,Vaibhav Sharma, Priyanka Zar . Study of Depression Analysis using Machine Learning Techniques. ISSN: 2278-3075, Volume-8, Issue-7C2, May 2019
D. Calderon-Vilca, W. I. Wun-Rafael and R. Miranda-Loarte, "Simulation of suicide tendency by using machine learning," 2017 36th International Conference of the Chilean Computer Science Society (SCCC), Arica, Chile, 2017, pp. 1-6, doi: 10.1109/SCCC.2017.8405128.
Hanai, Tuka & Ghassemi, Mohammad & Glass, James. (2018). Detecting Depression with Audio/Text Sequence Modeling of Interviews. 1716-1720. 10.21437/Interspeech.2018- 2522.
Hatton, Chris ; Paton, Lewis William ; McMillan, Dean ; Cussens, James ; Gilbody, Simon ; Tiffin, Paul Alexander. / Predicting persistent depressive symptoms in older adults : a machine learning approach to personalised mental healthcare. In: Journal of affective disorders. 2019 ; Vol. 246. pp. 857-860. /digital-2021-global-overview-report.
Hussain J. et al. (2015) SNS Based Predictive Model for Depression. In: Geissbühler  A., Demongeot J., Mokhtari M., Abdulrazak B., Aloulou H. (eds)  Inclusive Smart Cities  and e-Health. ICOST 2015. Lecture Notes in Computer Science, vol 9102. Springer,
Deshpande and V. Rao, "Depression detection using emotion artificial intelligence," 2017 International Conference on Intelligent Sustainable Systems (ICISS), Palladam, India, 2017, pp. 858-862, doi: 10.1109/ISS1.2017.8389299.
H. Abd El-Jawad, R. Hodhod and Y. M. K. Omar, "Sentiment Analysis of Social Media Networks Using Machine Learning," 2018 14th International Computer Engineering Conference (ICENCO), Cairo, Egypt, 2018, pp. 174-176, doi: 10.1109/ICENCO.2018.8636124.
M. Aldarwish and H. F. Ahmad, "Predicting Depression Levels Using Social Media Posts," 2017 IEEE 13th International Symposium on Autonomous Decentralized System (ISADS), Bangkok, 2017, pp. 277-280, doi: 10.1109/ISADS.2017.41.
Rathi, A. Malik, D. Varshney, R. Sharma and S. Mendiratta, "Sentiment Analysis of Tweets Using Machine Learning Approach," 2018 Eleventh International Conference on Contemporary Computing (IC3), Noida, India, 2018, pp. 1-3, doi: 10.1109/IC3.2018.8530517.
Marissa Walsh, Pharm.D., Mental health statistics 2021. Available at: Accessed Jan 21, 2021
Mariya Khan, Zoha Rizvi, Muhammad Zakir Shaikh, Warda Kazmi, and Anum Shaikh, “Design and Implementation of Intelligent Human Stress Monitoring System,” International Journal of Innovation and Scientific Research, vol. 10, no. 1, pp. 179–190, October 2014.
Melissa NS, Margaret L, Shannon J S, Nicholas BA. Detection of Adolescent Depression from Speech Using Optimised Spectral Roll-Off Parameters. Biomed J Sci &Tech Res 5(1)- 2018. BJSTR. MS.ID.001156. DOI: 10.26717/ BJSTR.2018.05.001156.
Raichur, N., Lonakadi, N., & Mural, P. (2017). Detection of Stress Using Image Processing and Machine Learning Techniques. International journal of engineering and technology, 9, 1-8.
M. Chaware, Chaitanya Makashir, Chinmayi Athavale, Manali Athavale, Tejas Baraskar. Stress Detection Methodology based on Social Media Network: A Proposed Design. 3, January 2020 ISSN: 2278-3075.
 Shatte ABR, Hutchinson DM, Teague SJ. Machine learning in mental health: a scoping review of methods and applications. Psychol Med. 2019 Jul;49(9):1426-1448. doi: 10.1017/S0033291719000151. Epub 2019 Feb 12. PMID: 30744717.
Simon Kemp. Digital 2021: Global Overview Report. Available at: Accessed Jan 27, 2021
Nguyen, D. Phung, B. Dao, S. Venkatesh and M. Berk, "Affective and Content Analysis of Online Depression Communities," in IEEE Transactions on Affective Computing, vol. 5, no. 3, pp. 217-226, 1 July-Sept. 2014, doi: 10.1109/TAFFC.2014.2315623.
Tate AE, McCabe RC, Larsson H, Lundström S, Lichtenstein P, Kuja-Halkola R. Predicting mental health problems in adolescence using machine learning techniques. PLoS One. 2020 Apr 6;15(4):e0230389. doi: 10.1371/journal.pone.0230389. PMID: 32251439; PMCID: PMC7135284.
World Health Organization. Depression is a mental disorder. Available at: Accessed Jan 30, 2021