Prediction of generalized anxiety levels during the Covid-19 pandemic: A machine learning-based modeling approach.

The rapid spread of the Covid-19 outbreak led many countries to enforce precautionary measures such as complete lockdowns. These lifestyle-altering measures caused a significant increase in anxiety levels globally. For that reason, decision-makers are in dire need of methods to prevent potential public mental crises. Machine learning has shown its effectiveness in the early prediction of several diseases. Therefore, this study aims to classify two-class and three-class anxiety problems early by utilizing a dataset collected during the Covid-19 pandemic in Saudi Arabia. The data was collected from 3017 participants from all regions of the Kingdom via an online survey containing questions to identify factors influencing anxiety levels, followed by questions from the GAD-7, a screening tool for Generalized Anxiety Disorders. The prediction models were built using the Support Vector Machine classifier for its robust outcomes in medical-related data and the J48 Decision Tree for its interpretability and comprehensibility. Experimental results demonstrated promising results for the early classification of two-class and three-class anxiety problems. As for comparing Support Vector Machine and J48, the Support Vector Machine classifier outperformed the J48 Decision Tree by attaining a classification accuracy of 100%, precision of 1.0, recall of 1.0, and f-measure of 1.0 using 10 features.

Anxiety Levels Amid the COVID-19 Lockdown in Saudi Arabia.

OBJECTIVE: To assess the prevalence of anxiety and factors associated with it during the peak of the outbreak in Saudi Arabia. MATERIALS AND METHODS: This cross-sectional research screened the general public using the Generalized Anxiety Disorder Scale-7 to detect anxiety levels. The questionnaire was distributed online during May 2020, while lockdowns were enforced. A total of 3017 respondents from all five main regions of Saudi Arabia completed the survey. The prevalence of anxiety was measured. Chi-square and logistic regression analyses were executed to determine associated factors with anxiety during peak lockdown. RESULTS: About 19.6% of the respondents possessed a moderate to severe level of anxiety during the pandemic. Western, Northern, and Eastern regions of Saudi Arabia were found to be the most anxious. Female participants had 5.3% higher levels of anxiety compared to male counterparts. The youngest age group (18 to 19 years), most of them were students, reported the highest frequency of anxiety (28.7%). Divorced and single participants had a higher level of anxiety compared to married ones. After adjusted with other variables, living with a family member with risk of the COVID-19 was the best predictor assessing anxiety amid peak lockdown (OR: 1.8, 95% CI: 1.4-2.2). CONCLUSION: Notable anxiety prevailed during the initial phase of the COVID-19 outbreak in Saudi Arabia. The presence of vulnerable subjects in the family augments this psychological disorder considerably. Our findings promulgate a need to inculcate nation-wide strategies to enforce public health emergency preparedness plans to mitigate the adverse psychological effects of outbreaks.