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Machine Learning Based Depression, Anxiety, and Stress Predictive Model During COVID-19 Crisis

Fahd N. Al-Wesabi1,2,*, Hadeel Alsolai3, Anwer Mustafa Hilal4, Manar Ahmed Hamza4, Mesfer Al Duhayyim5, Noha Negm6,7

1 Department of Computer Science, King Khalid University, Muhayel Aseer, Saudi Arabia
2 Faculty of Computer and IT, Sana’a University, Sana’a, Yemen
3 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Saudi Arabia
4 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia
5 Department of Natural and Applied Sciences, College of Community-Aflaj, Prince Sattam bin Abdulaziz University, Saudi Arabia
6 Computer Science Department, King Khaled University, KSA
7 Faculty of Science, Mathematics and Computer Science Department, Menoufia University, Egypt

* Corresponding Author: Fahd N. Al-Wesabi. Email: email

Computers, Materials & Continua 2022, 70(3), 5803-5820.


Corona Virus Disease-2019 (COVID-19) was reported at first in Wuhan city, China by December 2019. World Health Organization (WHO) declared COVID-19 as a pandemic i.e., global health crisis on March 11, 2020. The outbreak of COVID-19 pandemic and subsequent lockdowns to curb the spread, not only affected the economic status of a number of countries, but it also resulted in increased levels of Depression, Anxiety, and Stress (DAS) among people. Therefore, there is a need exists to comprehend the relationship among psycho-social factors in a country that is hypothetically affected by high levels of stress and fear; with tremendously-limiting measures of social distancing and lockdown in force; and with high rates of new cases and mortalities. With this motivation, the current study aims at investigating the DAS levels among college students during COVID-19 lockdown since they are identified as a highly-susceptible population. The current study proposes to develop Intelligent Feature Subset Selection with Machine Learning-based DAS predictive (IFSSML-DAS) model. The presented IFSSML-DAS model involves data preprocessing, Feature Subset Selection (FSS), classification, and parameter tuning. Besides, IFSSML-DAS model uses Group Gray Wolf Optimization based FSS (GGWO-FSS) technique to reduce the curse of dimensionality. In addition, Beetle Swarm Optimization based Least Square Support Vector Machine (BSO-LSSVM) model is also employed for classification in which the weight and bias parameters of the LSSVM model are optimally adjusted using BSO algorithm. The performance of the proposed IFSSML-DAS model was tested using a benchmark DASS-21 dataset and the results were investigated under different measures. The outcome of the study suggests the development of specialized programs to handle DAS among population so as to overcome COVID-19 crisis.


Cite This Article

APA Style
Al-Wesabi, F.N., Alsolai, H., Hilal, A.M., Hamza, M.A., Duhayyim, M.A. et al. (2022). Machine learning based depression, anxiety, and stress predictive model during COVID-19 crisis. Computers, Materials & Continua, 70(3), 5803-5820.
Vancouver Style
Al-Wesabi FN, Alsolai H, Hilal AM, Hamza MA, Duhayyim MA, Negm N. Machine learning based depression, anxiety, and stress predictive model during COVID-19 crisis. Comput Mater Contin. 2022;70(3):5803-5820
IEEE Style
F.N. Al-Wesabi, H. Alsolai, A.M. Hilal, M.A. Hamza, M.A. Duhayyim, and N. Negm "Machine Learning Based Depression, Anxiety, and Stress Predictive Model During COVID-19 Crisis," Comput. Mater. Contin., vol. 70, no. 3, pp. 5803-5820. 2022.


cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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