ArticleInformatics in medicine unlocked2022
Prediction of generalized anxiety levels during the Covid-19 pandemic: A machine learning-based modeling approach.
Article in Informatics in medicine unlocked, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Methodological guidance on clinical prediction models in mental health research.Psychological medicine · 2026Review
- Intersection of gender and class in the assessment of self-rated health in the Spanish population: a classification tree study.International journal for equity in health · 2026Article
- Lifestyle, psychological and demographic predictors of anxiety: insights from a large-scale survey and machine learning analysis.Frontiers in psychiatry · 2026Article
- Extending Anxiety Detection from Multimodal Wearables in Controlled Conditions to Real-World Environments.Sensors (Basel, Switzerland) · 2025Article
- A machine learning model to predict privacy fatigued users from social media personalized advertisements.Scientific reports · 2024Article
- Psychosomatic Disorder: The Current Implications and Challenges.Cardiovascular & hematological agents in medicinal chemistry · 2024Review
- Testing the bipolar assumption of Singer-Loomis Type Deployment Inventory for Korean adults using classification and multidimensional scaling.Frontiers in psychology · 2023Article
- The Relationship between Exercise and Mental Health Outcomes during the COVID-19 Pandemic: From the Perspective of Hope.International journal of environmental research and public health · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.