Title: Predicting anxiety disorders and suicide tendency using machine learning: a review

Authors: Theodore Kotsilieris; Emmanuel Pintelas; Ioannis E. Livieris; Panagiotis Pintelas

Addresses: Department of Business Administration (LAIQDA Lab), Technological Educational Institute of Peloponnese, GR 241-00, Greece ' Department of Electrical and Computer Engineering, University of Patras, GR 265-00, Greece ' Department of Computer and Informatics Engineering (DISK Lab), Technological Educational Institute of Western Greece, GR 263-34, Greece ' Department of Mathematics, University of Patras, GR 265-00, Greece

Abstract: Anxiety disorders constitute the largest group and the most common type of mental disorders. At the same time, machine learning techniques can be used for analysing a patient's history and diagnose problems imitating the human reasoning or in making logical decisions. This work reviews the main concepts and applications of machine learning techniques in predicting anxiety disorder types. Seventeen (17) studies were considered, that applied machine learning techniques for predicting anxiety disorders and five (5) 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 utilised for predicting the disorder.

Keywords: machine learning; generalised anxiety disorder; panic disorder; agoraphobia; social anxiety disorder; posttraumatic stress disorder; suicide tendency.

DOI: 10.1504/IJMEI.2020.111040

International Journal of Medical Engineering and Informatics, 2020 Vol.12 No.6, pp.599 - 608

Received: 22 Aug 2018
Accepted: 02 Feb 2019
Published online: 06 Nov 2020 *

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