Please use this identifier to cite or link to this item: http://hdl.handle.net/10889/10981
Title: Reviewing machine learning techniques for predicting anxiety disorders.
Authors: Theodore, Kotsilieris
Emmanouil, Pintelas
Ioannis, Livieris
Panagiotis, Pintelas
Keywords: Machine learning
Generalized anxiety disorder
Panic disorder
Agoraphobia
Social anxiety disorder
Posttraumatic stress disorder
Abstract: Anxiety disorders are a type of mental disorders characterized by important feelings of fear and anxiety. In recent years, 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 and suicide tendency, using machine learning techniques. Eighteen (18) studies were examined, revealing that machine learning techniques can be used for predicting anxiety disorders and two (2) additional studies were examined for predicting suicide tendencies. 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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