Computational Intelligence in Depression Detection

Md. Rahat Shahriar Zawad, Md. Yeaminul Haque, M. Shamim Kaiser, Mufti Mahmud, Tianhua Chen

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


According to the World Health Organisation, depression is the prime contributor to mental disability worldwide. Depression is a severe threat to people’s public and private lives because it causes catastrophic alterations in feelings and emotions. The recent rise in mental health issues and major depressive disorder has spurred many depression detection studies. Computational intelligence-based depression detection has piqued the scientific community’s interest due to its increased efficiency and low mistake rate. This work presented a systematic review of recent works on computational intelligence-based depression detection based on their detection models, preprocessing, and data types. Discussing the findings, frameworks for social media, smartphone data, image/video and biosignal based depression detection were suggested. Finally, challenges and future research scopes in depression detection using computational intelligence have also been discussed.
Original languageEnglish
Title of host publicationArtificial Intelligence in Healthcare
Subtitle of host publicationRecent Applications and Developments
EditorsTianhua Chen, Jenny Carter, Mufti Mahmud, Arjab Singh Khuman
PublisherSpringer Singapore
Number of pages19
ISBN (Electronic)9789811952722
ISBN (Print)9789811952715
Publication statusPublished - 26 Oct 2022

Publication series

NameBrain Informatics and Health
PublisherSpringer Singapore
ISSN (Print)2367-1742
ISSN (Electronic)2367-1750


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