A review of machine learning prediction methods for anxiety disorders
Abstract
Anxiety disorders are a type of mental disorders characterized by important feelings of fear and anxiety. Recently the evolution of machine learning techniques has helped greatly to develop tools assisting doctors to predict mental disorders and support patient care. In this work, a comparative literature search was conducted on research for the prediction of specific types of anxiety disorders, using machine learning techniques. Sixteen (16) studies were selected and examined, revealing that machine learning techniques can be used for effectively predicting anxiety disorders. The accuracy of the results varies according to the type of anxiety disorder and the type of methods utilized for predicting the disorder. We can deduce that significant work has been done on the prediction of anxiety using machine learning techniques. However, in the future we may achieve higher accuracy scores and that could lead to a better treatment support for patients.
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AI may allow us to investigate which psychological interventions and EMIs work best for which patients to tailor psychotherapy to patients' needs in real- or close to real-time during the therapeutic process.
“However, AI may allow us to investigate which psychological interventions and EMIs work best for which patients to tailor psychotherapy to patients’ needs in real- or close to real-time during the therapeutic process [71].”
From Using Artificial Intelligence to Enhance Ongoing Psychological Interventions for Emotional Problems in Real- or Close to Real-Time: A Systematic Review (2022) · cites this paper as [71]
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- Authors
- Pintelas, E. G., Kotsilieris, T., Livieris, I. E., & Pintelas, P.
- Affiliations
- University of Patras (GR)
- Technological Educational Institute of Peloponnese (GR)
- Technological Educational Institute of Western Greece (GR)
- Venue
- Proceedings of the 8th International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion
- Publisher
- ACM
- Published
- 2018-06-20
- Pages
- 8-15
- OpenAlex
- W2910593944
- Cited by
- 61
- References
- 48
- Citations by year
- 2026: 7 · 2025: 9 · 2024: 12 · 2023: 13 · 2022: 6 · 2021: 7 · 2020: 5 · 2019: 2
- FWCI
- 4.07
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion (DSAI) (publishedVersion)
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Anxiety, Depression, Psychometrics, Treatment, Cognitive Processes
- Keywords
- anxiety disorders, machine learning prediction, mental disorder prediction, diagnostic accuracy, clinical decision support, treatment support
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