Volume 7, Issue 4 (2-2020)                   JCP 2020, 7(4): 7-27 | Back to browse issues page

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Aboureihani Mohammadi M, Fadaei Moghadam Heydarabadi M, Zardary S, Heysieattalab S. Identification Psychological Disorders Based on Data in Virtual Environments Using Machine Learning. JCP. 2020; 7 (4) :7-27
URL: http://jcp.khu.ac.ir/article-1-3218-en.html
, heysieattalab@gmail.com‬‬‬‬
Abstract:   (1489 Views)
Introduction: Psychological disorders is one of the most problematic and important issue in todaychr('39')s society. Early prognosis of these disorders matters because receiving professional help at the appropriate time could improve the quality of life of these patients. Recently, researches use social media as a form of new tools in identifying psychological disorder. It seems that through the use of social networks can get longitudinal reports about situations of peoplechr('39')s lives like marriage, birth of child, losing a job, divorce, unpleasant events etc. as the primary evidence indicator of hidden mental or behavioral problems. Nowadays, personal contents that the users shared can be useful in the identification of the levels of their mental health. Based on this, a number of researchers tried to take step in predicting some of the mental disorders like depression, suicide tendency, bipolar, anxiety and so on considering social network data by using artificial intelligence from web the Data (Pupmed, Springer, ProQuest, Scopus, Science direct, Google Scholar, Magiran). Therefore, because of the complexity of identifying mental disorders by using common methods and also for the increase of prediction accuracy, researchers used some branches of artificial intelligence like machine learning to identify the users that are in need of psychological help.
Methods: Based on prisma this article aims to systematically review the articles in the field of mental health through searching the main keywords of diagnosis and prediction of mental disorders combining with machine learning world without considering the dates of their publication.
Results: Our study showed that most of these studies have been done on depressive disorder, among which the machine learning model was used  predictive power with 42% accuracy among the reviewed articles had the least prediction power and with 87% accuracy the most prediction power. Conclusion: It seems that computational psychology based on machine learning methods could help in identifying and choosing the appropriate treatment of disorders like depression, post-traumatic stress disorder, bipolar and suicide in the users of social media like Instagram, Twitter and Facebook. Although, there are so many developments in this field, there are still some faults in these methods like ethical issues related to invasion peoplechr('39')s privacy and also the complexity and interference of many factors identifying disorders.
Thus, it should be mentioned that these methods need a wider and more extensive developments and in the future by improvement in this field, researchers will be able to investigate and predict more accurately the disorders of the users of the social networks in larger scales.
     
Type of Study: Applicable | Subject: Special
Received: 2020/02/11 | Accepted: 2020/06/3 | Published: 2020/06/3

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