research-article

A review of machine learning prediction methods for anxiety disorders

Authors:

Emmanuel G. Pintelas,

Theodore Kotsilieris,

Ioannis E. Livieris,

Panagiotis PintelasAuthors Info & Claims

DSAI '18: Proceedings of the 8th International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion

Pages 8 - 15

https://doi.org/10.1145/3218585.3218587

Published: 20 June 2018 Publication History

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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Index Terms

A review of machine learning prediction methods for anxiety disorders
1. Applied computing
  1. Life and medical sciences
    1. Health care information systems
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Machine learning theory

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DSAI '18: Proceedings of the 8th International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion

June 2018

365 pages

ISBN:9781450364676

DOI:10.1145/3218585

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 20 June 2018

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DSAI 2018: 8th International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion

June 20 - 22, 2018

Thessaloniki, Greece

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DSAI '18 Paper Acceptance Rate 17 of 23 submissions, 74%;

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https://doi.org/10.59681/2175-4411.v16.iEspecial.2024.1333
Gautam RSharma M(2024)Computational Approaches for Anxiety and Depression: A Meta- Analytical PerspectiveICST Transactions on Scalable Information Systems10.4108/eetsis.623211Online publication date: 14-Aug-2024
https://doi.org/10.4108/eetsis.6232
Dhariwal NSengupta NMadiajagan MPatro KKumari PAbdel Samee NTadeusiewicz RPławiak PPrakash A(2024)A pilot study on AI-driven approaches for classification of mental health disordersFrontiers in Human Neuroscience10.3389/fnhum.2024.137633818Online publication date: 10-Apr-2024
https://doi.org/10.3389/fnhum.2024.1376338
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