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Growing Science » International Journal of Data and Network Science » Mental health and long COVID status prediction among recovered COVID-19 patients: A comparison of machine learning methods

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International Journal of Data and Network Science
ISSN 2561-8156 (Online) - ISSN 2561-8148 (Print)
Quarterly Publication
Volume 8 Issue 4 pp. 2383-2398, 2024

Mental health and long COVID status prediction among recovered COVID-19 patients: A comparison of machine learning methods Pages 2383-2398 Right click to download the paper Download PDF

Authors: Tran Anh Tuan, Win Win Myo, Le Thanh Thao Trang, Nguyen Thi The Nhan, Tran Dai An, Dao Thi Thanh Loan

📋 Author Affiliations:
T.A. Tuan ORCID 1, W.W. Myo ORCID 2, L.T.T. Trang3, N.T.T. Nhan4, T.D. An5, D.T.T. Loan6
1 School of Informatics, Walailak University, Nakhon Si Thammarat, Thailand, Informatics Innovation Center of Excellence (IICE), Walailak University, Nakhon Si Thammarat, Thailand
2 University of Information Technology, Yangon, Myanmar
3 Dong Thap Medical College, Dong Thap, Viet Nam
4 Pasteur Institute in Ho Chi Minh city, Ho Chi Minh city, Viet Nam
5 Dong Thap Provincial Center for Disease Control, Dong Thap, Viet Nam
6 Dak Lak College of Pedagogy, Dak Lak, Viet Nam
doi 10.5267/j.ijdns.2024.5.018
1 Source: Scopus
Crossref 1 Source: CrossRef

🔑 Keywords: Predictive Model, Machine Learning, Mental health, Long COVID, COVID-19

Abstract: The COVID-19 pandemic has led to different health outcomes, including long COVID (LCo) and mental health (MH) disorders, impacting millions globally. To enable early healthcare diagnosis, including the prediction of MH conditions and LCo, various research studies have utilized machine learning (ML) techniques. However, there is still a gap in understanding the mental health of recovered COVID-19 patients with long COVID using ML techniques. This study aims to bridge this gap by developing and evaluating ML models, including support vector machine, multilayer perceptron (MLP), k-nearest neighbor, gradient boosting, voting classifier, and extreme gradient boosting, tailored for mental health and long COVID datasets from recovered COVID-19 patients. Additionally, feature selection methods, e.g., Recursive Feature Elimination (RFE) and Extra Trees (ET), and optimized models with hyper-parameter tuning will be employed. Our experiments utilize the dataset of recovered COVID-19 patients. Among these ML models, the MLP with ET-based features achieved the highest accuracy and AUC scores in this dataset, with 1.00 and 0.97 ± 0.02, respectively. The research reveals the high prevalence and risk factors of mental health disorders and long COVID from the dataset. These findings will contribute to personalized healthcare strategies for individuals navigating the complexities of post-COVID-19 recovery, integrating machine learning insights into mental health and long COVID support.

How to cite this paper
APA: Tuan, T., Myo, W., Trang, L., Nhan, N., An, T & Loan, D. (2024). Mental health and long COVID status prediction among recovered COVID-19 patients: A comparison of machine learning methods. International Journal of Data and Network Science, 8(4), 2383-2398.
Chicago/Turabian: Tuan, T., Myo, W., Trang, L., Nhan, N., An, T & Loan, D. 2024. "Mental health and long COVID status prediction among recovered COVID-19 patients: A comparison of machine learning methods." International Journal of Data and Network Science 8, no. 4 (2024): 2383-2398.
AMA: Tuan, T., Myo, W., Trang, L., Nhan, N., An, T & Loan, D. Mental health and long COVID status prediction among recovered COVID-19 patients: A comparison of machine learning methods. International Journal of Data and Network Science. 2024;8(4):2383-2398.

References
Afrash, M. R., Shanbehzadeh, M., & Kazemi-Arpanahi, H. (2022). Predicting risk of mortality in COVID-19 hospitalized patients using hybrid machine learning algorithms. Journal of Biomedical Physics & Engineering, 12(6), 611.
Aiyegbusi, O. L., Hughes, S. E., Turner, G., Rivera, S. C., McMullan, C., Chandan, J. S., Haroon, S., Price, G., Davies, E. H., Nirantharakumar, K., & others. (2021). Symptoms, complications and management of long COVID: a review. Journal of the Royal Society of Medicine, 114(9), 428–442.
Aljrees, T. (2024). Improving prediction of cervical cancer using KNN imputer and multi-model ensemble learning. Plos One, 19(1), e0295632.
Almajali, D., & Masadeh, R. (2021). Antecedents of students’ perceptions of online learning through covid-19 pandemic in Jordan. International Journal of Data and Network Science, 5(4), 587–592.
Arshed, M. A., Qureshi, W., Khan, M. U. G., & Jabbar, M. A. (2021). Symptoms based Covid-19 disease diagnosis using machine learning approach. 2021 International Conference on Innovative Computing (ICIC), 1–7.
Batool, A., & Byun, Y.-C. (2024). Towards Improving Breast Cancer Classification using an Adaptive Voting Ensemble Learning Algorithm. IEEE Access.
Bergstra, J., Bardenet, R., Bengio, Y., & Kégl, B. (2011). Algorithms for hyper-parameter optimization. Advances in Neu-ral Information Processing Systems, 24.
Chadaga, K., Prabhu, S., Sampathila, N., Chadaga, R., Umakanth, S., Bhat, D., & GS, S. K. (2024). Explainable artificial intelligence approaches for COVID-19 prognosis prediction using clinical markers. Scientific Reports, 14(1), 1783.
Chen, T., & Guestrin, C. (2016). Xgboost: A scalable tree boosting system. Proceedings of the 22nd Acm Sigkdd Interna-tional Conference on Knowledge Discovery and Data Mining, 785–794.
Cho, S.-E., Geem, Z. W., & Na, K.-S. (2021). Predicting depression in community dwellers using a machine learning algo-rithm. Diagnostics, 11(8), 1429.
Chung, J., & Teo, J. (2023). Single classifier vs. ensemble machine learning approaches for mental health prediction. Brain Informatics, 10(1), 1–10.
Davis, H. E., McCorkell, L., Vogel, J. M., & Topol, E. J. (2023). Long COVID: major findings, mechanisms and recom-mendations. Nature Reviews Microbiology, 21(3), 133–146.
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.
Dhariwal, N., Sengupta, N., Madiajagan, M., Patro, K. K., Kumari, P. L., Abdel Samee, N., Tadeusiewicz, R., Pławiak, P., & Prakash, A. J. (2024). A pilot study on AI-driven approaches for classification of mental health disorders. Frontiers in Human Neuroscience, 18, 1376338.
Do Duy, C., Nong, V. M., Van, A. N., Thu, T. D., Do Thu, N., & Quang, T. N. (2020). COVID-19-related stigma and its as-sociation with mental health of health-care workers after quarantine in Vietnam. Psychiatry and Clinical Neuroscienc-es, 74(10), 566.
Duong, K. N. C., Le Bao, T. N., Nguyen, P. T. L., Van, T. V., Lam, T. P., Gia, A. P., Anuratpanich, L., Van, B. V., & others. (2020). Psychological impacts of COVID-19 during the first nationwide lockdown in Vietnam: web-based, cross-sectional survey study. JMIR Formative Research, 4(12), e24776.
Elnagar, A., Alnazzawi, N., Afyouni, I., Shahin, I., Nassif, A., & Salloum, S. (2022). An empirical study of e-learning post-acceptance after the spread of COVID-19. International Journal of Data and Network Science, 6(3), 669–682.
Engel, F. D., da Fonseca, G. G. P., Cechinel-Peiter, C., Backman, C., da Costa, D. G., & de Mello, A. L. S. F. (2023). Im-pact of the COVID-19 Pandemic on the Experiences of Hospitalized Patients: A Scoping Review. Journal of Patient Safety, 19(2), e46.
Geurts, P., Ernst, D., & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63, 3–42.
Gupta, A., Jain, V., & Singh, A. (2022). Stacking ensemble-based intelligent machine learning model for predicting post-COVID-19 complications. New Generation Computing, 40(4), 987–1007.
Guyon, I., & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of Machine Learning Re-search, 3(Mar), 1157–1182.
Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002). Gene selection for cancer classification using support vector ma-chines. Machine Learning, 46, 389–422.
Hossen, M. J., Ramanathan, T. T., Al Mamun, A., & others. (2024a). An Ensemble Feature Selection Approach-Based Ma-chine Learning Classifiers for Prediction of COVID-19 Disease. International Journal of Telemedicine and Applica-tions, 2024.
Hossen, M. J., Ramanathan, T. T., Al Mamun, A., & others. (2024b). An Ensemble Feature Selection Approach-Based Ma-chine Learning Classifiers for Prediction of COVID-19 Disease. International Journal of Telemedicine and Applica-tions, 2024.
Hussein, A. N., Makki, S. V. A.-D., & Al-Sabbagh, A. (2023). Comprehensive study: machine learning approaches for COVID-19 diagnosis. International Journal of Electrical and Computer Engineering (IJECE), 13(5), 5681–5695.
Islam, M. N., Islam, M. S., Shourav, N. H., Rahman, I., Faisal, F. Al, Islam, M. M., & Sarker, I. H. (2024). Exploring post-COVID-19 health effects and features with advanced machine learning techniques. Scientific Reports, 14(1), 9884.
Jha, A., Abirami, M. S., & Kumar, V. (2022). Predictive Model for Depression and Anxiety Using Machine Learning Algo-rithms. International Conference on Deep Sciences for Computing and Communications, 133–147.
Jiang, S., Loomba, J., Sharma, S., & Brown, D. (2022). Vital measurements of hospitalized COVID-19 patients as a pre-dictor of long COVID: An EHR-based cohort study from the RECOVER program in N3C. 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 3023–3030.
Juliet, S., & others. (2023). Investigations on Machine Learning Models for Mental Health Analysis and Prediction. 2023 Second International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT), 1–7.
Kanrak, M., & Nonthapot, S. (2024). Analysis of the tourism network post the COVID-19 pandemic: Implications for revi-talization. International Journal of Data and Network Science, 8(3), 1781–1792.
Katiyar, K., Fatma, H., & Singh, S. (2024). Predicting Anxiety, Depression and Stress in Women Using Machine Learning Algorithms. In Combating Women’s Health Issues with Machine Learning (pp. 22–40). CRC Press.
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), 74756–74856.
Ku, W. L., & Min, H. (2024). Evaluating Machine Learning Stability in Predicting Depression and Anxiety Amidst Subjec-tive Response Errors. Healthcare, 12(6), 625.
Kumar, P., Chandra, S., & others. (2023). Prediction and comparison of psychological health during COVID-19 among In-dian population and Rajyoga meditators using machine learning algorithms. Procedia Computer Science, 218, 697–705.
Linh, H. N., Loan, N. T., Uyen, N. T. T., Nam, T. T., Phu, D. H., & others. (2024). Prevalence and risk factors associated with long COVID symptoms in children and adolescents in a southern province of Vietnam. Asian Pacific Journal of Tropical Medicine, 17(3), 119–128.
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.
Lovibond, S. H. (1995). Manual for the depression anxiety stress scales. Sydney Psychology Foundation.
Malik, S. S., & Khan, A. (2023). Anxiety, Depression and Stress prediction among College Students using Machine Learn-ing Algorithms. 2023 Second International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT), 1–5.
Nasir, A., Makki, S. V. A.-D., & Al-Sabbagh, A. (2024). Pandemia Prediction Using Machine Learning. PRZEGLĄD EL-EKTROTECHNICZNY, 5, 211–214.
Nguyen, H. V., & Byeon, H. (2022). Explainable Deep-Learning-Based Depression Modeling of Elderly Community after COVID-19 Pandemic. Mathematics, 10(23), 4408.
Nison, P., Vuttipittayamongkol, P., Boonyapuk, P., & Kemavuthanon, K. (2023). A Machine Learning Approach for De-pression Screening in College Students Based on Non-Clinical Information. 2023 International Conference On Cyber Management And Engineering (CyMaEn), 413–417.
Patel, M. A., Knauer, M. J., Nicholson, M., Daley, M., Van Nynatten, L. R., Cepinskas, G., & Fraser, D. D. (2023). Organ and cell-specific biomarkers of Long-COVID identified with targeted proteomics and machine learning. Molecular Medicine, 29(1), 26.
Patterson, B. K., Guevara-Coto, J., Yogendra, R., Francisco, E. B., Long, E., Pise, A., Mora, J., & Mora-Rodr\’\iguez, R. A. (2021). Immune-based prediction of COVID-19 severity and chronicity decoded using machine learning. Frontiers in Immunology, 12, 700782.
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.
Pfaff, E. R., Girvin, A. T., Bennett, T. D., Bhatia, A., Brooks, I. M., Deer, R. R., Dekermanjian, J. P., Jolley, S. E., Kahn, M. G., Kostka, K., & others. (2022). Identifying who has long COVID in the USA: a machine learning approach using N3C data. The Lancet Digital Health, 4(7), e532–e541.
Phu, D. H., Maneerattanasak, S., Shohaimi, S., Trang, L. T. T., Nam, T. T., Kuning, M., Like, A., Torpor, H., & Su-wanbamrung, C. (2023). Prevalence and factors associated with long COVID and mental health status among recov-ered COVID-19 patients in southern Thailand. PloS One, 18(7), e0289382.
Pramodhani, R. J., Vineela, P. S. S., Aseesh, V. S., Kumar, K., & Devi, B. S. K. (2022). Stress Prediction and Detection in Internet of Things using Learning Methods. 2022 4th International Conference on Inventive Research in Computing Applications (ICIRCA), 303–309.
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.
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.
Qasrawi, R., Amro, M., VicunaPolo, S., Al-Halawa, D. A., Agha, H., Seir, R. A., Hoteit, M., Hoteit, R., Allehdan, S., Behzad, N., & others. (2022). Machine learning techniques for predicting depression and anxiety in pregnant and post-partum women during the COVID-19 pandemic: A cross-sectional regional study. F1000Research, 11.
Rass, V., Beer, R., Schiefecker, A. J., Kofler, M., Lindner, A., Mahlknecht, P., Heim, B., Limmert, V., Sahanic, S., Pizzini, A., & others. (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.
Rezapour, M., & Hansen, L. (2022). A machine learning analysis of COVID-19 mental health data. Scientific Reports, 12(1), 14965.
Saha, R., Malviya, L., Jadhav, A., & Dangi, R. (2024). Early stage HIV diagnosis using optimized ensemble learning tech-nique. Biomedical Signal Processing and Control, 89, 105787.
Saltzman, L. Y., Longo, M., & Hansel, T. C. (2023). Long-COVID stress symptoms: Mental health, anxiety, depression, or posttraumatic stress. Psychological Trauma: Theory, Research, Practice, and Policy.
Sarmiento Varón, L., González-Puelma, J., Medina-Ortiz, D., Aldridge, J., Alvarez-Saravia, D., Uribe-Paredes, R., & Na-varrete, M. A. (2023). The role of machine learning in health policies during the COVID-19 pandemic and in long COVID management. Frontiers in Public Health, 11, 1140353.
Scikit-learn. (2024). Cross-validation: evaluating estimator performance. https://scikit-learn.org/stable/modules/cross_validation.html
Singh, S., Gupta, H., Singh, P., & Agrawal, A. P. (2022). Comparative Analysis of Machine Learning Models to Predict Depression, Anxiety and Stress. 2022 11th International Conference on System Modeling & Advancement in Research Trends (SMART), 1199–1203.
Sudre, C. H., Murray, B., Varsavsky, T., Graham, M. S., Penfold, R. S., Bowyer, R. C., Pujol, J. C., Klaser, K., Antonelli, M., Canas, L. S., & others. (2021). Attributes and predictors of long COVID. Nature Medicine, 27(4), 626–631.
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, 72–89.
Trang, L. T. T., Le, C. N., Chutipatana, N., Shohaimi, S., & Suwanbamrung, C. (2023). Prevalence and predictors of de-pression, anxiety, and stress among recovered COVID-19 patients in Vietnam. Roczniki Państwowego Zakładu Hi-gieny, 74(2).
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. 2022 International Conference Advancement in Data Science, E-Learning and Information Systems (ICADEIS), 1–5.
Tuan, T. A., Trang, L. T. T., An, T. D., Nghia, N. H., & Loan, D. T. T. (2024). An Analysis of Machine Learning for Detect-ing Depression, Anxiety, and Stress of Recovered COVID-19 Patients. Journal of Human, Earth, and Future, 5(1), 1–18.
Tyshchenko, Y. (2018). Depression and anxiety detection from blog posts data. Nature Precis. Sci., Inst. Comput. Sci., Univ. Tartu, Tartu, Estonia.
Vaishnavi, K., Kamath, U. N., Rao, B. A., & Reddy, N. V. S. (2022). Predicting mental health illness using machine learn-ing algorithms. Journal of Physics: Conference Series, 2161(1), 12021.
WHO. (2000). The Asia-Pacific perspective: redefining obesity and its treatment.
WHO. (2022). Post COVID-19 condition (Long COVID). https://www.who.int/europe/news-room/fact-sheets/item/post-covid-19-condition
WHO. (2024). COVID-19 epidemiological update – 12 April 2024. https://www.who.int/publications/m/item/covid-19-epidemiological-update-edition-166
Yazdani, A., Zahmatkeshan, M., Ravangard, R., Sharifian, R., & Shirdeli, M. (2022). Supervised machine learning ap-proach to COVID-19 detection based on clinical data. Medical Journal of the Islamic Republic of Iran, 36.
Zhang, Y., Chinchilli, V. M., Ssentongo, P., & Ba, D. M. (2024). Association of Long COVID with mental health disorders: a retrospective cohort study using real-world data from the USA. BMJ Open, 14(2), e079267.
Zoabi, Y., Deri-Rozov, S., & Shomron, N. (2021). Machine learning-based prediction of COVID-19 diagnosis based on symptoms. Npj Digital Medicine, 4(1), 3.
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