ArticlePloS one2021
Understanding importance of clinical biomarkers for diagnosis of anxiety disorders using machine learning models.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled it.
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Who cites it
11 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence techniques applied to anxiety disorders recognition: a systematic review.Frontiers in digital health · 2025Pooled it
- Candidate Biological Markers for Social Anxiety Disorder: A Systematic Review.International journal of molecular sciences · 2023Pooled it
- Socioeconomic determinants of malaria in Ugandan children: An interpretable machine learning approach for public health policy.PLOS digital health · 2026Article
- Artificial Intelligence-Based Methods and Omics for Mental Illness Diagnosis: A Review.Bioengineering (Basel, Switzerland) · 2025Review
- Machine learning-based prediction of anxiety disorders using blood metabolite and social trait data from the UK Biobank.Brain, behavior, & immunity - health · 2025Article
- Development of a machine learning-based multivariable prediction model for the naturalistic course of generalized anxiety disorder.Journal of anxiety disorders · 2025Article
- Article
- Development of an individualized dementia risk prediction model using deep learning survival analysis incorporating genetic and environmental factors.Alzheimer's research & therapy · 2024Article
- Exploring the Role of Artificial Intelligence in Mental Healthcare: Current Trends and Future Directions - A Narrative Review for a Comprehensive Insight.Risk management and healthcare policy · 2024Review
- Article
- Methodological and Quality Flaws in the Use of Artificial Intelligence in Mental Health Research: Systematic Review.JMIR mental health · 2023Review
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Anxiety disorders are a group of mental illnesses that cause constant and overwhelming feelings of anxiety and fear. Excessive anxiety can make an individual avoid work, school, family get-togethers, and other social situations that in turn might amplify these symptoms. According to the World Health Organization (WHO), one in thirteen persons globally suffers from anxiety. It is high time to understand the roles of various clinical biomarker measures that can diagnose the types of anxiety disorders. In this study, we apply machine learning (ML) techniques to understand the importance of a set of biomarkers with four types of anxiety disorders-Generalized Anxiety Disorder (GAD), Agoraphobia (AP), Social Anxiety Disorder (SAD) and Panic Disorder (PD). We used several machine learning models and extracted the variable importance contributing to a type of anxiety disorder. The study uses a sample of 11,081 Dutch citizens' data collected by the Lifelines, Netherlands. The results show that there are significant and low correlations among GAD, AP, PD and SAD and we extracted the variable importance hierarchy of biomarkers with respect to each type of anxiety disorder which will be helpful in designing the experimental setup for clinical trials related to influence of biomarkers on type of anxiety disorder.
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