Computational Approaches for Anxiety and Depression: A Meta-Analytical Perspective.

Gautam, Ritu; Sharma, Manik

INTRODUCTION: Psychological disorders are a critical issue in today's modern society, yet it remains to be continuously neglected. Anxiety and depression are prevalent psychological disorders that persuade a generous number of populations across the world and are scrutinized as global problems. METHODS: The three-step methodology is employed in this study to determine the diagnosis of anxiety and depressive disorders. In this survey, a systematic review of one hundred forty-one articles on depression and anxiety disorders using different traditional classifiers, metaheuristics, and deep learning techniques was done. RESULTS: The best performance and publication trends of traditional classifiers, metaheuristics and deep learning techniques have also been presented. Eventually, a comparison of these three techniques in the diagnosis of anxiety and depression disorders will be appraised. CONCLUSION: There is further scope in the diagnosis of anxiety disorders such as social anxiety disorder, phobia disorder, panic disorder, generalized anxiety, and obsessive-compulsive disorders. Already, a lot of work has been done on conventional approaches to the prognosis of these disorders. So, there is a need to scrutinize the prognosis of depression and anxiety disorders using the hybridization of metaheuristic and deep learning techniques. Also, the diagnosis of these two disorders among the academic fraternity using metaheuristic and deep learning techniques must be explored.

SOCIAL anxiety; ANXIETY disorders; OBSESSIVE-compulsive disorder; PANIC disorders; MENTAL depression

EAI Endorsed Transactions on Scalable Information Systems, 2025, Vol 12, Issue 1, p1

2032-9407

Academic Journal

10.4108/eetsis.6232