A Comprehensive Review on Anxiety, Stress and Depression Models Based on Machine Learning Algorithms
Keywords:
Anxiety, Depression, Machine Learning, Mental Health, Mental illness, Prediction, Psychological illness, StressAbstract
Anxiety, stress, and depression are the different stages of a person's mental illness that negatively impact their health, emotions, and social interactions. In the present time, 450 million people are suffering from mental illness, according to the World Health Organization (WHO). Thus, determining the mental illness in the earlier phase helps people achieve better health. However, determining the mental illness stage through medical procedures is time-consuming, costly, and requires monitoring patients for a long time. To overcome this issue, in the present time, machine learning algorithms are deployed to detect mental illness in the earlier phase by analysing the behaviour of the patients. Therefore, in this paper, we have done a comprehensive review of anxiety, stress, and depression models designed using machine learning algorithms. Initially, different stages of mental illness are explained and how they are differentiated from each other. Further, we have studied and analysed the machine learning algorithms and their various types. Next, we have explained the different stages used to build the anxiety, stress, and depression models using machine learning algorithms. After that, we have done a critical analysis of the existing models based on their aim, machine learning algorithm, input and output parameters, database, performance metrics, and tool used. Based on the critical analysis, we have given a detailed description of the tool, database, and performance metrics. Finally, we have identified some open research challenges and recommendations to enhance the existing models.
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A. Singh and D. Kumar, “Computer assisted identification of stress, anxiety, depression (SAD) in students: A state-of-the-art review,” Medical Engineering & Physics, vol. 110, p. 103900, Dec. 2022, doi: 10.1016/j.medengphy.2022.103900. [Online]. Available: http://dx.doi.org/10.1016/j.medengphy.2022.103900
L. I. Pearlin, “The Sociological Study of Stress,” Journal of Health and Social Behavior, vol. 30, no. 3, p. 241, Sep. 1989, doi: 10.2307/2136956. [Online]. Available: http://dx.doi.org/10.2307/2136956
S. Gulseren, L. Gulseren, Z. Hekimsoy, P. Cetinay, C. Ozen, and B. Tokatlioglu, “Depression, Anxiety, Health-Related Quality of Life, and Disability in Patients with Overt and Subclinical Thyroid Dysfunction,” Archives of Medical Research, vol. 37, no. 1, pp. 133–139, Jan. 2006, doi: 10.1016/j.arcmed.2005.05.008. [Online]. Available: http://dx.doi.org/10.1016/j.arcmed.2005.05.008
Alqudah et al., “About anxiety levels and anti‐anxiety drugs among quarantined undergraduate Jordanian students during COVID‐19 pandemic,” International Journal of Clinical Practice, vol. 75, no. 7, Apr. 2021, doi: 10.1111/ijcp.14249. [Online]. Available: http://dx.doi.org/10.1111/ijcp.14249
N. S. Mohd Shafiee and S. Mutalib, “Prediction of Mental Health Problems among Higher Education Student Using Machine Learning,” International Journal of Education and Management Engineering, vol. 10, no. 6, pp. 1–9, Dec. 2020, doi: 10.5815/ijeme.2020.06.01. [Online]. Available: http://dx.doi.org/10.5815/ijeme.2020.06.01
“The Malaysian Higher Education System - An Overview,” StudyMalaysia.com, Mar. 13, 2022. [Online]. Available: https://studymalaysia.com/education/higher-education-in-malaysia/the-malaysian-higher-education-system-an-overview
S. Shalev-Shwartz and S. Ben-David, “Understanding Machine Learning,” May 2014, doi: 10.1017/cbo9781107298019. [Online]. Available: http://dx.doi.org/10.1017/cbo9781107298019
D. Praveen Kumar, T. Amgoth, and C. S. R. Annavarapu, “Machine learning algorithms for wireless sensor networks: A survey,” Information Fusion, vol. 49, pp. 1–25, Sep. 2019, doi: 10.1016/j.inffus.2018.09.013. [Online]. Available: http://dx.doi.org/10.1016/j.inffus.2018.09.013
“What is Logistic Regression? - Statistics Solutions,” Statistics Solutions, Jun. 14, 2022. [Online]. Available: https://www.statisticssolutions.com/free-resources/directory-of-statistical-analyses/what-is-logistic-regression/
Sharda, Ramesh, Dursun Delen, Efraim Turban, J. Aronson, and T. Liang, “Business intelligence and analytics,” System for Decesion Support, 2014.
E. Garcia-Ceja, M. Riegler, T. Nordgreen, P. Jakobsen, K. J. Oedegaard, and J. Tørresen, “Mental health monitoring with multimodal sensing and machine learning: A survey,” Pervasive and Mobile Computing, vol. 51, pp. 1–26, Dec. 2018, doi: 10.1016/j.pmcj.2018.09.003. [Online]. Available: http://dx.doi.org/10.1016/j.pmcj.2018.09.003
L. Pierson, Data Science For Dummies. John Wiley & Sons, 2021.
X. Wang and C. Chen, “Research on competence evaluation system of data mining tools,” 2015 IEEE International Conference on Mechatronics and Automation (ICMA), Aug. 2015, doi: 10.1109/icma.2015.7237446. [Online]. Available: http://dx.doi.org/10.1109/icma.2015.7237446
“Best Programming Language For Me in 2018.” [Online]. Available: https://www.bestprogramminglanguagefor.me/
N. Nordin, Z. Zainol, M. H. Mohd Noor, and L. F. Chan, “An explainable predictive model for suicide attempt risk using an ensemble learning and Shapley Additive Explanations (SHAP) approach,” Asian Journal of Psychiatry, vol. 79, p. 103316, Jan. 2023, doi: 10.1016/j.ajp.2022.103316. [Online]. Available: http://dx.doi.org/10.1016/j.ajp.2022.103316
Priya, S. Garg, and N. P. Tigga, “Predicting Anxiety, Depression and Stress in Modern Life using Machine Learning Algorithms,” Procedia Computer Science, vol. 167, pp. 1258–1267, 2020, doi: 10.1016/j.procs.2020.03.442. [Online]. Available: http://dx.doi.org/10.1016/j.procs.2020.03.442
P. Kumar, S. Garg, and A. Garg, “Assessment of Anxiety, Depression and Stress using Machine Learning Models,” Procedia Computer Science, vol. 171, pp. 1989–1998, 2020, doi: 10.1016/j.procs.2020.04.213. [Online]. Available: http://dx.doi.org/10.1016/j.procs.2020.04.213
S. Bhatnagar, J. Agarwal, and O. R. Sharma, “Detection and classification of anxiety in university students through the application of machine learning,” Procedia Computer Science, vol. 218, pp. 1542–1550, 2023, doi: 10.1016/j.procs.2023.01.132. [Online]. Available: http://dx.doi.org/10.1016/j.procs.2023.01.132
Jain, Swati, et al, “A machine learning based depression analysis and suicidal ideation detection system using questionnaires and twitter,” 2019 IEEE students conference on engineering and systems (SCES). IEEE, 2019.
M. Simjanoski et al., “Lifestyle predictors of depression and anxiety during COVID-19: a machine learning approach,” Trends in Psychiatry and Psychotherapy, 2022, doi: 10.47626/2237-6089-2021-0365. [Online]. Available: http://dx.doi.org/10.47626/2237-6089-2021-0365
R. Chiong, G. S. Budhi, S. Dhakal, and F. Chiong, “A textual-based featuring approach for depression detection using machine learning classifiers and social media texts,” Computers in Biology and Medicine, vol. 135, p. 104499, Aug. 2021, doi: 10.1016/j.compbiomed.2021.104499. [Online]. Available: http://dx.doi.org/10.1016/j.compbiomed.2021.104499
S. M. Sarsam, H. Al-Samarraie, A. I. Alzahrani, C. S. Mon, and A. S. Shibghatullah, “Characterizing Suicide Ideation by Using Mental Disorder Features on Microblogs: A Machine Learning Perspective,” International Journal of Mental Health and Addiction, Nov. 2022, doi: 10.1007/s11469-022-00958-z. [Online]. Available: http://dx.doi.org/10.1007/s11469-022-00958-z
T. Richter, B. Fishbain, A. Markus, G. Richter-Levin, and H. Okon-Singer, “Using machine learning-based analysis for behavioral differentiation between anxiety and depression,” Scientific Reports, vol. 10, no. 1, Oct. 2020, doi: 10.1038/s41598-020-72289-9. [Online]. Available: http://dx.doi.org/10.1038/s41598-020-72289-9
M. D. Nemesure, M. V. Heinz, R. Huang, and N. C. Jacobson, “Predictive modeling of depression and anxiety using electronic health records and a novel machine learning approach with artificial intelligence,” Scientific Reports, vol. 11, no. 1, Jan. 2021, doi: 10.1038/s41598-021-81368-4. [Online]. Available: http://dx.doi.org/10.1038/s41598-021-81368-4
“R-Overview.”[Online].Available: https://www.tutorialspoint.com/r/r_overview.htm
“Weka Tutorial.” [Online]. Available: https://www.tutorialspoint.com/weka/index.htm
“SPSS Tutorial - javatpoint,” www.javatpoint.com. [Online]. Available: https://www.javatpoint.com/spss
“MATLAB - Overview.” [Online]. Available: https://www.tutorialspoint.com/matlab/matlab_overview.htm
“Sentiment140 dataset with 1.6 million tweets,” Kaggle, Sep. 13, 2017. [Online]. Available: https://www.kaggle.com/datasets/kazanova/sentiment140
“Reddit Self-reported Depression Diagnosis (RSDD) dataset.” [Online]. Available: https://georgetown-ir-lab.github.io/emnlp17-depression/
Swathi et al, “Stress detection and analysis in working environment using SVM classifier,” Dogo Rangsang Research Journal, vol. 12, pp. 668-670, 2022.
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