How to cite this paper
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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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