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.
|Title of host publication||Artificial Intelligence in Healthcare|
|Subtitle of host publication||Recent Applications and Developments|
|Editors||Tianhua Chen, Jenny Carter, Mufti Mahmud, Arjab Singh Khuman|
|Number of pages||19|
|Publication status||Published - 26 Oct 2022|
|Name||Brain Informatics and Health|