Advertisement

A Fuzzy Approach for the Diagnosis of Depression

  • Abhijit Thakur
  • Md. Sakibul Alam
  • Md. Rashidul Hasan Abir
  • Mahir Ashab Ahmed Kushal
  • Rashedur M. RahmanEmail author
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 769)

Abstract

The main objective of this study is to develop a software prototype for diagnosing the risk factors and grading depression in the developing region of South East Asia especially in Bangladesh. World Health Organization (WHO) identified depression to be the most prevalent psychological disorder and according to global burden of disease survey it will be the second leading cause of long term disability. For various social constructs depression or any kind of psychiatric disorder is considered a taboo subject. Hence it is very difficult to collect data on the context of a developing country like Bangladesh. We are using a hybrid model questionnaire based on Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) and Patient Health Questionnaire (PHQ-9) by consulting psychiatric experts of Bangladesh. This study proposed a model based on Fuzzy Logic (FL). An experimental study of the system was conducted using 50 anonymous medical dataset of depression patients’ cases obtained from two experts in psychiatry from National Mental Health Institute of Bangladesh and Sheikh Mujib Medical College Hospital.

Keywords

Depression model Risk diagnosis Fuzzy logic Neuro-fuzzy controller Hybrid tool Depression in Bangladesh 

Notes

Acknowledgements

The authors of this paper fully acknowledge the doctors, the psychiatrists and experts who have shared their valuable knowledge and help in collecting the required data, analyzing them, developing the correct system, and validating the results to make this research successful.

References

  1. 1.
    Chattopadhyay, S., Pratihar, D.K., De Sarkar, S.C.: Some studies on fuzzy clustering of psychosis data. Int. J. Bus. Intell. Data Min. 2, 143–159 (2007)Google Scholar
  2. 2.
    Chattopadhyay, S., Ray, P., Lee, M.B., Chen, H.S.: Towards the design of an e-health system for suicide prevention. In: Proceedings of the Eleventh IASTED International Conference on Artificial Intelligence, Palma de Mallorca, Spain, 2010, pp. 191–196Google Scholar
  3. 3.
    Chattopadhyay, S., Pratihar, D.K., De Sarkar, S.C.: Developing fuzzy classifiers to predict the chance of occurrence of adult psychoses. Knowl. Based Syst. 20, 479–497 (2008)CrossRefGoogle Scholar
  4. 4.
    Chattopadhyay, S., Pratihar, D.K., De Sarkar, S.C.: Fuzzy logicbased screening and prediction of adult psychoses: a novel approach. IEEE Trans. Syst. Man Cybern. A Syst. Hum. 39, 381–387 (2009)CrossRefGoogle Scholar
  5. 5.
    Chattopadhyay, S., Pratihar, D.K., De Sarkar, S.C.: Statistical modelling of psychoses data, Comput. Methods Programs Biomed. 100, 222–236 (2010)Google Scholar
  6. 6.
    Tai, Y.-M., Chiu, H.-W.: Artificial neural network analysis on suicide and self-harm history of Taiwanese soldiers. In: Proceedings of the Second International Conference on Innovative Computing, Information and Control (ICICIC), Kumamoto, Japan, p. 363 (2007)Google Scholar
  7. 7.
    Chattopadhyay, S., Kaur, P., Rabhi, F., Acharya, U.R.: Neural network approaches to grade adult depression. J. Med. Syst. 36(5), 2803–2815 (2012)Google Scholar
  8. 8.
    de Carvalho, L.M.F., Nassar, S.M., de Azevedo, F.M., de Carvalho, H.J.T., Monteiro, L.L., Rech, C.M.Z.: A neurofuzzy system to support in the diagnostic of epileptic events and non-epileptic events using different arithmetical operations. Arq. Neuropsiquiatr. 66(2a), 179–183 (2008)CrossRefGoogle Scholar
  9. 9.
    Ekong, V.E., Inyang, U.G., Onibere, E.A.: Intelligent decision support system for depression diagnosis based on neuro-fuzzy-CBR hybrid. Modern Appl. Sci. 6(7) (2012).  https://doi.org/10.5539/mas.v6n7p79
  10. 10.
    Marks, I., Kenwright, M., McDonough, M., Whittaker, M., O’Brien, T., Mataix-Cols, D.: Saving clinicians’ time by delegating routine aspects of therapy to a computer: a randomised controlled trial in Panic/Phobia disorder. Psychol. Med. 34, 9–17 (2004).  https://doi.org/10.1017/S003329170300878XCrossRefGoogle Scholar
  11. 11.
    Ashish, K., Dasari, A., Chattopadhyay, S., Hui, N.B.: Genetic-neuro-fuzzy system for grading depression. Appl. Comput. Inform. (2017).  https://doi.org/10.1016/j.aci.2017.05.005Google Scholar
  12. 12.
    Chattopadhyay, S.: Psyconsultant I: a DSM-IV-based screening tool for adult psychiatric disorders in Indian rural health center. Internet J. Med. Inform. 3 (Serial Online) (2006)Google Scholar
  13. 13.
    Chattopadhyay, S.: A computerized tool for screening of adult psychiatric illnesses: a third-world perspective. J. Clin. Inform. Telemed. 3, 1–5 (2006)Google Scholar
  14. 14.
    Suhasini, A., Palanivel, S., Ramalingam, V.: Multi decision support model for psychiatry problem. Int. J. Comput. Appl. 1, 61–69 (2010)Google Scholar
  15. 15.
  16. 16.
  17. 17.
    Kushal, D.: Expert opinion on parameter establishment. Unpublished raw data (2017)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Abhijit Thakur
    • 1
  • Md. Sakibul Alam
    • 1
  • Md. Rashidul Hasan Abir
    • 1
  • Mahir Ashab Ahmed Kushal
    • 1
  • Rashedur M. Rahman
    • 1
    Email author
  1. 1.Department of Electrical and Computer EngineeringNorth South UniversityDhakaBangladesh

Personalised recommendations