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Modelling the Psychological Impact of COVID-19 in Saudi Arabia Using Machine Learning

Mohammed A. Aleid1, Khaled A. Z. Alyamani2, Mohieddine Rahmouni2,3, Theyazn H. H. Aldhyani2,*, Nizar Alsharif4, Mohammed Y. Alzahrani4

1 College of Education, King Faisal University, Al-Ahsa, 31982, Saudi Arabia
2 Community College in Abqaiq, King Faisal University, Al-Ahsa, 31982, Saudi Arabia
3 Department of Economics and Quantitative Methods, ESSECT, University of Tunis, Tunis, Tunisia
4 Department of Computer Engineering and Science, Al-Baha University, Al-Bahah, Saudi Arabia

* Corresponding Author: Theyazn H. H. Aldhyani. Email: email

(This article belongs to the Special Issue: Application of Big Data Analytics in the Management of Business)

Computers, Materials & Continua 2021, 67(2), 2029-2047. https://doi.org/10.32604/cmc.2021.014873

Abstract

This article aims to assess health habits, safety behaviors, and anxiety factors in the community during the novel coronavirus disease (COVID-19) pandemic in Saudi Arabia based on primary data collected through a questionnaire with 320 respondents. In other words, this paper aims to provide empirical insights into the correlation and the correspondence between socio-demographic factors (gender, nationality, age, citizenship factors, income, and education), and psycho-behavioral effects on individuals in response to the emergence of this new pandemic. To focus on the interaction between these variables and their effects, we suggest different methods of analysis, comprising regression trees and support vector machine regression (SVMR) algorithms. According to the regression tree results, the age variable plays a predominant role in health habits, safety behaviors, and anxiety. The health habit index, which focuses on the extent of behavioral change toward the commitment to use the health and protection methods, is highly affected by gender and age factors. The average monthly income is also a relevant factor but has contrasting effects during the COVID-19 pandemic period. The results of the SVMR model reveal a strong positive effect of income, with R2 values of 99.59%, 99.93% and 99.88% corresponding to health habits, safety behaviors, and anxiety.

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APA Style
Aleid, M.A., Alyamani, K.A.Z., Rahmouni, M., Aldhyani, T.H.H., Alsharif, N. et al. (2021). Modelling the psychological impact of COVID-19 in saudi arabia using machine learning. Computers, Materials & Continua, 67(2), 2029-2047. https://doi.org/10.32604/cmc.2021.014873
Vancouver Style
Aleid MA, Alyamani KAZ, Rahmouni M, Aldhyani THH, Alsharif N, Alzahrani MY. Modelling the psychological impact of COVID-19 in saudi arabia using machine learning. Comput Mater Contin. 2021;67(2):2029-2047 https://doi.org/10.32604/cmc.2021.014873
IEEE Style
M.A. Aleid, K.A.Z. Alyamani, M. Rahmouni, T.H.H. Aldhyani, N. Alsharif, and M.Y. Alzahrani "Modelling the Psychological Impact of COVID-19 in Saudi Arabia Using Machine Learning," Comput. Mater. Contin., vol. 67, no. 2, pp. 2029-2047. 2021. https://doi.org/10.32604/cmc.2021.014873

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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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