A review of machine learning prediction methods for anxiety disorders Print

Em. Pintelas, T. Kotsilieris, I.E. Livieris, and P. Pintelas. A review of machine learning prediction methods for anxiety disorders. In ACM 8th International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Infoexclusion, 2018.



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.