Design and implementation of a web-based fuzzy expert system for diagnosing depressive disorder
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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.
KeywordsDepressive disorder Medical diagnosis Expert system Knowledge acquisition Fuzzy logic
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.
- 1.World Health Organization (WHO) (2015) Depression, Fact sheet N369. http://www.who.int/mediacentre/factsheets/fs369/en/. Accessed 8 Oct 2015
- 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
- 4.Fassler D, Lynne SD (1997) Help me, i’m sad: recognizing, treating and preventing childhood and adolescent depression. Viking, New YorkGoogle Scholar
- 5.Shortliffe EH (1986) Medical expert systems-knowledge tools for physicians. In Medical informatics [Special Issue]. West J Med 145:830–839Google Scholar
- 6.Buchanan BG, Shortliffe EH (1984) Rule-based expert systems: the MYCIN experiments of the stanford heuristic programming project. Addison-Wesley, ReadingGoogle Scholar
- 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
- 22.Giarratano J, Riley G (1998) Expert systems: principles and programming, 3rd edn. PWS Pub, BostonGoogle Scholar
- 25.Friedman-Hill E (2003) Jess in action: java rule-based systems. Manning Publications Co., Greenwich CTGoogle Scholar
- 26.Beck AT, Steer RA, Brown GK (1996) BDI-II, Beck depression inventory: manual. San Antonio, Psychological CorpGoogle Scholar
- 30.The Jess FAQ (2013) http://www.jessrules.com/jess/FAQ.shtml. Accessed 29 Jul 2015
- 31.Boehm BW (1984) Verifying and validating software requirements and design specifications. IEEE Soft IEEE Software 1: 75–88Google Scholar