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Applied Intelligence

, Volume 48, Issue 5, pp 1302–1313 | Cite as

Design and implementation of a web-based fuzzy expert system for diagnosing depressive disorder

  • Hassan Ali Mohammadi Motlagh
  • Behrouz Minaei Bidgoli
  • Ali Akbar Parvizi Fard
Article
  • 191 Downloads

Abstract

Mental health problems have always existed in human life. However, factors such as lifestyle changes and industrialization affect modern human mental health. A common mental illness is depressive disorder, and a large number of patients are not even aware they have it. Due to the harmful influences of depressive disorder on quality of life, timely and accurate diagnosis is a matter of extreme prominence. The aim of this study is to design a web-based expert system for diagnosing depression using the fuzzy Delphi method by estimating the weights and importance of depression symptoms. Fuzzy Logic is adopted to calculate the level of depression. Two thresholds are obtained, namely lack of depression and severe depression. Finally, the level of depression for each person is estimated by virtue of the calculated value’s proximity to these two opposing points. Java Expert System Shell is used to build the knowledge base. Sensitivity and specificity analysis are performed with 238 participants. The proposed system appears helpful for everyone, from ordinary persons to specialists in medical environments. It can also be useful to train psychology students in the area of diagnostic reasoning.

Keywords

Depressive disorder Medical diagnosis Expert system Knowledge acquisition Fuzzy logic 

Notes

Acknowledgements

We would like to express our appr to the specialists at the Department of Psychiatry (Psychiatry and Clinical Psychology), Kermanshah University of Medical Sciences, who filled out the questionnaires, as well as the psychological consultants and participants who helped evaluate the system.

References

  1. 1.
    World Health Organization (WHO) (2015) Depression, Fact sheet N369. http://www.who.int/mediacentre/factsheets/fs369/en/. Accessed 8 Oct 2015
  2. 2.
    National Alliance on Mental Illness (NAMI) Major depression (2013) https://www.nami.org/getattachment/Learn-More/Fact-Sheet-Library/Depression-Fact-Sheet.pdf. Accessed 14 Sept 2015
  3. 3.
    American Psychiatric Association (APA) (2013) Diagnostic and statistical manual of mental disorders: DSM-5, 5th edn. American Psychiatric Publishing, ArlingtonCrossRefGoogle Scholar
  4. 4.
    Fassler D, Lynne SD (1997) Help me, i’m sad: recognizing, treating and preventing childhood and adolescent depression. Viking, New YorkGoogle Scholar
  5. 5.
    Shortliffe EH (1986) Medical expert systems-knowledge tools for physicians. In Medical informatics [Special Issue]. West J Med 145:830–839Google Scholar
  6. 6.
    Buchanan BG, Shortliffe EH (1984) Rule-based expert systems: the MYCIN experiments of the stanford heuristic programming project. Addison-Wesley, ReadingGoogle Scholar
  7. 7.
    Groth-Marnat G (2000) Visions of clinical assessment: then, now, and a brief history of the future. J Clin Psychol 56:349–365CrossRefGoogle Scholar
  8. 8.
    Keleş A, Keleş A, Yavuz U (2011) Expert system based on neuro-fuzzy rules for diagnosis breast cancer. Expert Syst Appl 38(5):5719–5726CrossRefGoogle Scholar
  9. 9.
    Ochab M, Wajs W (2016) Expert system supporting an early prediction of the bronchopulmonary dysplasia. Comput Biol Med 69:236–244CrossRefGoogle Scholar
  10. 10.
    Akçura MT, Ozdemir ZD (2014) Drug prescription behavior and decision support systems. Decis Support Syst 57:395–405CrossRefGoogle Scholar
  11. 11.
    Das S, Guha D, Dutta B (2016) Medical diagnosis with the aid of using fuzzy logic and intuitionistic fuzzy logic. Appl Intell 45(2):850–867CrossRefGoogle Scholar
  12. 12.
    Goethe J, Bronzino J (1995) An expert system for monitoring psychiatric treatment. IEEE Eng Med Biology Mag 14:776–780CrossRefGoogle Scholar
  13. 13.
    Razzouk D, Mari JJ, Shirakawa I, Wainer J, Sigulem D (2006) Decision support system for the diagnosis of schizophrenia disorders. Braz J Med Biol Res 39:119–128CrossRefGoogle Scholar
  14. 14.
    Casado-Lumbreras C, Rodríguez-gonzález A, Álvarez-rodríguez JM, Colomo-Palacios R (2012) PsyDis: towards a diagnosis support system for psychological disorders. Expert Syst Appl 39(13):11391–11403CrossRefGoogle Scholar
  15. 15.
    Finkelstein J, Lapshin O (2007) Reducing depression stigma using a web-based program. Int J Med Inform 76(10):726–734CrossRefGoogle Scholar
  16. 16.
    Gibbons RD, Hooker G, Finkelman MD, Weiss DJ, Pilkonis PA, Frank E, Kupfer DJ (2013) The CAD-MDD: a computerized adaptive diagnostic screening tool for depression. J Clinical Psychiatry 74(7):669–674CrossRefGoogle Scholar
  17. 17.
    Valenza G, Gentili C, Lanatà A, Scilingo EP (2013) Mood recognition in bipolar patients through the PSYCHE platform: preliminary evaluations and perspectives. Artif Intell Med 57(1):49–58CrossRefGoogle Scholar
  18. 18.
    Ekong VE, Inyang UG, Onibere EA (2012) Intelligent decision support system for depression diagnosis based on neuro-fuzzy-CBR hybrid. Modern Appl Sci MAS 6(7):79–88Google Scholar
  19. 19.
    Gibbons RD, Weiss DJ, Pilkonis PA, Frank E, Moore T, Kim JB, Kupfer DJ (2014) Development of the CAT-ANX: a computerized adaptive test for anxiety. Amer J Psychiatry AJP 171(1):187–194CrossRefGoogle Scholar
  20. 20.
    Luxton DD (2014) Artificial intelligence in psychological practice: current and future applications and implications. Prof Psychol Res Prac 45(5):332–339CrossRefGoogle Scholar
  21. 21.
    Jackson P (1998) Introduction to expert systems, 3rd edn. Addison Wesley, USAzbMATHGoogle Scholar
  22. 22.
    Giarratano J, Riley G (1998) Expert systems: principles and programming, 3rd edn. PWS Pub, BostonGoogle Scholar
  23. 23.
    Durkin J (1994) Expert systems: design and development. Macmillan, New YorkzbMATHGoogle Scholar
  24. 24.
    Siler W, Buckley JJ (2005) Fuzzy expert systems and fuzzy reasoning: theory and applications. Wiley, ChichesterzbMATHGoogle Scholar
  25. 25.
    Friedman-Hill E (2003) Jess in action: java rule-based systems. Manning Publications Co., Greenwich CTGoogle Scholar
  26. 26.
    Beck AT, Steer RA, Brown GK (1996) BDI-II, Beck depression inventory: manual. San Antonio, Psychological CorpGoogle Scholar
  27. 27.
    Kaufmann A, Gupta MM (1985) Introduction to fuzzy arithmetic: theory and applications. Van Nostrand Reinhold, New YorkzbMATHGoogle Scholar
  28. 28.
    Yang T, Hung CC (2007) Multiple-attribute decision making methods for plant layout design problem. Robot Comput Integr Manuf 23(1):126–137CrossRefGoogle Scholar
  29. 29.
    Dong WM, Wong FS (1987) Fuzzy weighted averages and implementation of the extension principle. Fuzzy Sets Syst 21:183–199MathSciNetCrossRefzbMATHGoogle Scholar
  30. 30.
    The Jess FAQ (2013) http://www.jessrules.com/jess/FAQ.shtml. Accessed 29 Jul 2015
  31. 31.
    Boehm BW (1984) Verifying and validating software requirements and design specifications. IEEE Soft IEEE Software 1: 75–88Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Hassan Ali Mohammadi Motlagh
    • 1
  • Behrouz Minaei Bidgoli
    • 2
  • Ali Akbar Parvizi Fard
    • 3
  1. 1.Faculty of Technical and EngineeringQom UniversityQomIran
  2. 2.Department of Computer EngineeringIran University of Science and TechnologyTehranIran
  3. 3.Department of PsychiatryKermanshah University of Medical SciencesKermanshahIran

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