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Computational Intelligence in Depression Detection

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Artificial Intelligence in Healthcare

Part of the book series: Brain Informatics and Health ((BIH))

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Abstract

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.

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Acknowledgements

MM is supported by the AI-TOP (2020-1-UK01-KA201-079167) and DIVERSASIA (618615-EPP-1-2020-1-UKEPPKA2-CBHEJP) projects funded by the European Commission under the Erasmus+ programme.

Author information

Authors and Affiliations

  1. Bangladesh University of Professionals, Dhaka, Bangladesh

    Md. Rahat Shahriar Zawad & Md. Yeaminul Haque

  2. Institute of Information Technology, Jahangirnagar University, Savar, Dhaka, 1342, Bangladesh

    M Shamim Kaiser

  3. Applied Intelligence and Informatics Lab, Wazed Miah Science Research Centre (WMSRC), Jahangirnagar University, Savar, Dhaka, 1342, Bangladesh

    M Shamim Kaiser

  4. Department of Computer Science, Nottingham Trent University, Nottingham, NG11 8NS, UK

    Mufti Mahmud

  5. Computing and Informatics Research Centre, Nottingham Trent University, Nottingham, NG11 8NS, UK

    Mufti Mahmud

  6. Medical Technologies Innovation Facility, Nottingham Trent University, Nottingham, NG11 8NS, UK

    Mufti Mahmud

  7. Department of Computer Science, University of Huddersfield, Huddersfield, HD1 3DH, UK

    Tianhua Chen

Authors
  1. Md. Rahat Shahriar Zawad
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  2. Md. Yeaminul Haque
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  3. M Shamim Kaiser
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  4. Mufti Mahmud
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  5. Tianhua Chen
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Corresponding authors

Correspondence to M Shamim Kaiser or Mufti Mahmud .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Huddersfield, Huddersfield, UK

    Tianhua Chen

  2. Department of Computer Science, University of Huddersfield, Holmfirth, UK

    Jenny Carter

  3. Department of Computer Science, Nottingham Trent University, Nottingham, UK

    Mufti Mahmud

  4. Computing, Engineering and Media, De Montfort University, Leicester, UK

    Arjab Singh Khuman

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Rahat Shahriar Zawad, M., Yeaminul Haque, M., Kaiser, M.S., Mahmud, M., Chen, T. (2022). Computational Intelligence in Depression Detection. In: Chen, T., Carter, J., Mahmud, M., Khuman, A.S. (eds) Artificial Intelligence in Healthcare. Brain Informatics and Health. Springer, Singapore. https://doi.org/10.1007/978-981-19-5272-2_7

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  • DOI: https://doi.org/10.1007/978-981-19-5272-2_7

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  • Print ISBN: 978-981-19-5271-5

  • Online ISBN: 978-981-19-5272-2

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