An Analysis of Machine Learning for Detecting Depression, Anxiety, and Stress of Recovered COVID-19 Patients

Tran Anh Tuan, Le Thanh Thao Trang, Tran Dai An, Nguyen Huu Nghia, Dao Thi Thanh Loan


Objectives: This study explores different machine learning models (KNN: k-nearest neighbor, MLP: Multilayer Perceptron, SVM: Support Vector Machine) to identify the optimal model for accurate and rapid mental health detection among the recovered COVID-19 patients. Other techniques are also investigated, such as feature selection (Recursive Feature Elimination (RFE) and Extra Trees (ET) methods) and hyper-parameter tuning, to achieve a system that could effectively and quickly indicate mental health. Method/Analysis: To achieve the objectives, the study employs a dataset collected from recovered COVID-19 patients, encompassing information related to depression, anxiety, and stress. Machine learning models are utilized in the analysis. Additionally, feature selection methods and hyper-parameter tuning techniques are explored to enhance the model’s predictive capabilities. The performance of each model is assessed based on accuracy metrics. Findings: The experimental results show that SVM is the most suitable model for accurately predicting an individual’s mental health among recovered COVID-19 patients (accuracy ≥ 0.984). Furthermore, the ET method is more effective than the RFE method for feature selection in the anxiety and stress datasets. Novelty/Improvement:The study lies in the understanding of predictive modeling for mental health and provides insights into the choice of models and techniques for accurate and early detection.


Doi: 10.28991/HEF-2024-05-01-01

Full Text: PDF


Predictive Model; Machine Learning; Depression; Anxiety; Stress; DAS; Mental Health; COVID-19.


Lopez-Leon, S., Wegman-Ostrosky, T., Perelman, C., Sepulveda, R., Rebolledo, P. A., Cuapio, A., & Villapol, S. (2021). More than 50 long-term effects of COVID-19: a systematic review and meta-analysis. Scientific Reports, 11(1), 16144. doi:10.1038/s41598-021-95565-8.

Rass, V., Beer, R., Schiefecker, A. J., Kofler, M., Lindner, A., Mahlknecht, P., Heim, B., Limmert, V., Sahanic, S., Pizzini, A., Sonnweber, T., Tancevski, I., Scherfler, C., Zamarian, L., Bellmann-Weiler, R., Weiss, G., Djamshidian, A., Kiechl, S., Seppi, K., … Helbok, R. (2021). Neurological outcome and quality of life 3 months after COVID-19: A prospective observational cohort study. European Journal of Neurology, 28(10), 3348–3359. doi:10.1111/ene.14803.

de Oliveira Almeida, K., Nogueira Alves, I. G., de Queiroz, R. S., de Castro, M. R., Gomes, V. A., Santos Fontoura, F. C., Brites, C., & Neto, M. G. (2023). A systematic review on physical function, activities of daily living and health-related quality of life in COVID-19 survivors. Chronic Illness, 19(2), 279–303. doi:10.1177/17423953221089309.

Pei, H., Wu, Q., Xie, Y., Deng, J., Jiang, L., & Gan, X. (2021). A Qualitative Investigation of the Psychological Experiences of COVID-19 Patients Receiving Inpatient Care in Isolation. Clinical Nursing Research, 30(7), 1113–1120. doi:10.1177/10547738211024807.

Engel, F. D., Da Fonseca, G. G. P., Cechinel-Peiter, C., Backman, C., Da Costa, D. G., & De Mello, A. L. S. F. (2023). Impact of the COVID-19 Pandemic on the Experiences of Hospitalized Patients: A Scoping Review. Journal of Patient Safety, 19(2), E46–E52. doi:10.1097/PTS.0000000000001084.

Sun, N., Wei, L., Shi, S., Jiao, D., Song, R., Ma, L., ... & Wang, H. (2020). A qualitative study on the psychological experience of caregivers of COVID-19 patients. American journal of infection control, 48(6), 592-598. doi:10.1016/j.ajic.2020.03.018.

ADAA. (2023). Depression symptoms. Anxiety and Depression Association of America, Maryland, United States. Available online: (accessed on May 2023).

NIMH Resources. (2023). Depression symptoms. National Institutes of Mental Health, Maryland, United States. Available online: (accessed on May 2023).

Tyshchenko, Y. (2018). Depression and anxiety detection from blog posts data. Institute of Computer Science Computer Science Curriculum, University of Tartu, Tartu, Estonia.

Kralj, M. M. (1989). Life-change stress and stress symptoms. Journal of College Student Development, 30, 333.

Marks, H. (2023). Stress Symptoms. Health & Balance. Stress Management, WedMD website, 1–4. Available online:

Kroenke, K., Spitzer, R. L., & Williams, J. B. W. (2001). The PHQ-9: Validity of a brief depression severity measure. Journal of General Internal Medicine, 16(9), 606–613. doi:10.1046/j.1525-1497.2001.016009606.x.

Oei, T. P. S., Sawang, S., Goh, Y. W., & Mukhtar, F. (2013). Using the Depression Anxiety Stress Scale 21 (DASS-21) across cultures. International Journal of Psychology, 48(6), 1018–1029. doi:10.1080/00207594.2012.755535.

Bakkeli, N. Z. (2023). Predicting psychological distress during the COVID-19 pandemic: do socioeconomic factors matter?. Social Science Computer Review, 41(4), 1227-1251. doi:10.1177/08944393211069622.

Garriga, R., Mas, J., Abraha, S., Nolan, J., Harrison, O., Tadros, G., & Matic, A. (2022). Machine learning model to predict mental health crises from electronic health records. Nature medicine, 28(6), 1240-1248. doi:10.1038/s41591-022-01811-5.

Pabreja, K., Singh, A., Singh, R., Agnihotri, R., Kaushik, S., & Malhotra, T. (2021). Stress prediction model using machine learning. Proceedings of International Conference on Artificial Intelligence and Applications, 57-68. doi:10.1007/978-981-15-4992-2_6.

Vilca, L. W., Chávez, B. V., Fernández, Y. S., Caycho-Rodríguez, T., & White, M. (2023). Impact of the fear of catching COVID-19 on mental health in undergraduate students: A Predictive Model for anxiety, depression, and insomnia. Current Psychology, 42(16), 13231-13238. doi:10.1007/s12144-021-02542-5.

Dinga, R., Marquand, A. F., Veltman, D. J., Beekman, A. T., Schoevers, R. A., van Hemert, A. M., ... & Schmaal, L. (2018). Predicting the naturalistic course of depression from a wide range of clinical, psychological, and biological data: a machine learning approach. Translational Psychiatry, 8(1), 241. doi:10.1038/s41398-018-0289-1.

Perzow, S. E., Hennessey, E. M. P., Hoffman, M. C., Grote, N. K., Davis, E. P., & Hankin, B. L. (2021). Mental health of pregnant and postpartum women in response to the COVID-19 pandemic. Journal of affective disorders reports, 4, 100123. doi:10.1016/j.jadr.2021.100123.

Qasrawi, R., Amro, M., VicunaPolo, S., Abu Al-Halawa, D., Agha, H., Abu Seir, R., Hoteit, M., Hoteit, R., Allehdan, S., Behzad, N., Bookari, K., AlKhalaf, M., Al-Sabbah, H., Badran, E., & Tayyem, R. (2022). Machine learning techniques for predicting depression and anxiety in pregnant and postpartum women during the COVID-19 pandemic: A cross-sectional regional study. F1000 Research, 11. doi:10.12688/f1000research.110090.1.

Nguyen, H. V., & Byeon, H. (2022). Explainable Deep-Learning-Based Depression Modeling of Elderly Community after COVID-19 Pandemic. Mathematics, 10(23), 4408. doi:10.3390/math10234408.

Hawes, M. T., Schwartz, H. A., Son, Y., & Klein, D. N. (2023). Predicting adolescent depression and anxiety from multi-wave longitudinal data using machine learning. Psychological Medicine, 53(13), 6205-6211. doi:10.1017/S0033291722003452.

Trivedi, N. K., Tiwari, R. G., Witarsyah, D., Gautam, V., Misra, A., & Nugraha, R. A. (2022). Machine Learning Based Evaluations of Stress, Depression, and Anxiety. Proceedings - International Conference Advancement in Data Science, E-Learning and Information Systems, ICADEIS 2022, 1–5. doi:10.1109/ICADEIS56544.2022.10037336.

Chung, J., & Teo, J. (2023). Single classifier vs. ensemble machine learning approaches for mental health prediction. Brain Informatics, 10(1), 1–10. doi:10.1186/s40708-022-00180-6.

Ajith Sankar, R., & Juliet, S. (2023). Investigations on Machine Learning Models for Mental Health Analysis and Prediction. 2023 2nd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2023, 1–7. doi:10.1109/ICEEICT56924.2023.10157385.

Deplancke, C., Somerville, M. P., Harrison, A., & Vuillier, L. (2023). It’s all about beliefs: Believing emotions are uncontrollable is linked to symptoms of anxiety and depression through cognitive reappraisal and expressive suppression. Current Psychology, 42(25), 22004-22012. doi:10.1007/s12144-022-03252-2.

Nemesure, M. D., Heinz, M. V., Huang, R., & Jacobson, N. C. (2021). Predictive modeling of depression and anxiety using electronic health records and a novel machine learning approach with artificial intelligence. Scientific reports, 11(1), 1980. doi:10.1038/s41598-021-81368-4.

Malik, S. S., & Khan, A. (2023). Anxiety, Depression and Stress prediction among College Students using Machine Learning Algorithms. 2023 2nd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2023, 1–5. doi:10.1109/ICEEICT56924.2023.10157693.

Prout, T. A., Zilcha-Mano, S., Aafjes-van Doorn, K., Békés, V., Christman-Cohen, I., Whistler, K., Kui, T., & Di Giuseppe, M. (2020). Identifying Predictors of Psychological Distress during COVID-19: A Machine Learning Approach. Frontiers in Psychology, 11, 586202. doi:10.3389/fpsyg.2020.586202.

Nison, P., Vuttipittayamongkol, P., Boonyapuk, P., & Kemavuthanon, K. (2023). A Machine Learning Approach for Depression Screening in College Students Based on Non-Clinical Information. 2023 International Conference on Cyber Management and Engineering, CyMaEn 2023, 413–417. doi:10.1109/CyMaEn57228.2023.10051001.

Zulfiker, M. S., Kabir, N., Biswas, A. A., Nazneen, T., & Uddin, M. S. (2021). An in-depth analysis of machine learning approaches to predict depression. Current Research in Behavioral Sciences, 2, 100044. doi:10.1016/j.crbeha.2021.100044.

Knolle, F., Ronan, L., & Murray, G. K. (2021). The impact of the COVID-19 pandemic on mental health in the general population: a comparison between Germany and the UK. BMC psychology, 9, 1-17. doi:10.1186/s40359-021-00565-y.

Rezapour, M., & Hansen, L. (2022). A machine learning analysis of COVID-19 mental health data. Scientific Reports, 12(1), 14965. doi:10.1038/s41598-022-19314-1.

Cho, S. E., Geem, Z. W., & Na, K. S. (2021). Predicting depression in community dwellers using a machine learning algorithm. Diagnostics, 11(8), 1429. doi:10.3390/diagnostics11081429.

Jain, T., Jain, A., Hada, P. S., Kumar, H., Verma, V. K., & Patni, A. (2021). Machine Learning Techniques for Prediction of Mental Health. In 2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA), 1606-1613. doi:10.1109/ICIRCA51532.2021.9545061.

Trang, L. T. T., Le, C. N., Chutipatana, N., Shohaimi, S., & Suwanbamrung, C. (2023). Prevalence and Predictors of Depression, Anxiety, and Stress among Recovered Covid-19 Patients in Vietnam. Annals of the National Institute of Hygiene, 74(2), 217–230. doi:10.32394/rpzh.2023.0261.

Phu, D. H., Maneerattanasak, S., Shohaimi, S., Trang, L. T. T., Nam, T. T., Kuning, M., Like, A., Torpor, H., & Suwanbamrung, C. (2023). Prevalence and factors associated with long COVID and mental health status among recovered COVID-19 patients in southern Thailand. PLoS ONE, 18(7 July), 289382. doi:10.1371/journal.pone.0289382.

Suwanbamrung, C., Pongtalung, P., Trang, L. T. T., Phu, D. H., & Nam, T. T. (2023). Levels and risk factors associated with depression, anxiety, and stress among COVID-19 infected adults after hospital discharge in a Southern Province of Thailand. Journal of Public Health and Development, 21(1), 72–89. doi:10.55131/jphd/2023/210106.

Huynh, G., Nguyen, H. V., Vo, L. Y., Le, N. T., & Nguyen, H. T. N. (2022). Assessment of insomnia and associated factors among patients who have recovered from COVID-19 in Vietnam. Patient preference and adherence, 1637-1647. doi:10.2147/PPA.S371563.

Iguyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of Machine Learning Research, 3(March), 1157–1182.

Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002). Gene selection for cancer classification using support vector machines. Machine Learning, 46(1–3), 389–422. doi:10.1023/A:1012487302797.

Geurts, P., Ernst, D., & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42. doi:10.1007/s10994-006-6226-1.

Vaishnavi, K., Kamath, U. N., Rao, B. A., & Reddy, N. S. (2022). Predicting mental health illness using machine learning algorithms. Journal of Physics: Conference Series, IOP Publishing, 2161, 012021. doi:10.1088/1742-6596/2161/1/012021.

Singh, S., Gupta, H., Singh, P., & Agrawal, A. P. (2022). Comparative Analysis of Machine Learning Models to Predict Depression, Anxiety and Stress. Proceedings of the 2022 11th International Conference on System Modeling and Advancement in Research Trends, SMART 2022, 1199–1203. doi:10.1109/SMART55829.2022.10047752.

Jha, A., Abirami, M. S., & Kumar, V. (2023). Predictive Model for Depression and Anxiety Using Machine Learning Algorithms. Communications in Computer and Information Science, 1719 CCIS, 133–147. doi:10.1007/978-3-031-27622-4_11.

Priya, A., Garg, S., & Tigga, N. P. (2020). Predicting anxiety, depression and stress in modern life using machine learning algorithms. Procedia Computer Science, 167, 1258-1267. doi:10.1016/j.procs.2020.03.442.

Shobhika, Kumar, P., & Chandra, S. (2022). Prediction and comparison of psychological health during COVID-19 among Indian population and Rajyoga meditators using machine learning algorithms. Procedia Computer Science, 218, 697–705. doi:10.1016/j.procs.2023.01.050.

Nayan, M. I. H., Uddin, M. S. G., Hossain, M. I., Alam, M. M., Zinnia, M. A., Haq, I., ... & Methun, M. I. H. (2022). Comparison of the performance of machine learning-based algorithms for predicting depression and anxiety among University Students in Bangladesh: A result of the first wave of the COVID-19 pandemic. Asian Journal of Social Health and Behavior, 5(2), 75-84. doi:10.4103/shb.shb_38_22.

Kim, S. W., & Chang, M. C. (2023). The usefulness of machine learning analysis for predicting the presence of depression with the results of the Korea National Health and Nutrition Examination Survey. Annals of Palliative Medicine, 12(4), 748–756. doi:10.21037/apm-23-78.

Lovibond, S. H., & Lovibond, P. F. (1995). Manual for the Depression Anxiety Stress Scales. Psychology Foundation of Australia, 56, 42.

Full Text: PDF

DOI: 10.28991/HEF-2024-05-01-01


  • There are currently no refbacks.

Copyright (c) 2024 Tran Anh Tuan, Le Thanh Thao Tran, Tran Dai An, Nguyen Huu Nghia, Dao Thi Thanh Loan