Diagnosing diseases with inaccurate signs and symptoms is the basis for designing a fuzzy logic medical system. Fuzzy inference is a method of processing information based on expert rules set in a fuzzy form. The article discusses the possibility of applying a fuzzy expert system for diagnosing gastrointestinal parasitic diseases. The authors consider a fuzzy logic system for diagnosing enterobiasis , one of the most common paediatric gastrointestinal parasitic diseases. For this purpose, the authors revealed the key symptoms of enterobiasis and developed an algorithm for the functioning of a fuzzy expert system to diagnose gastrointestinal parasitic diseases. The algorithm applied the knowledge base of a fuzzy expert diagnostic system, where the base provides structured information. The process then converted the input data into linguistic variables, conducted using the membership function in the fuzzy knowledge base. A triangular fuzzifier type was selected to complete the converting process, after which the study determined the interval of fuzzy values of the linguistic variables. The structure of fuzzy rules for diagnosing enterobiasis was then developed. In the next step, the fuzzy inference engine directed the input data to be mapped into their respective weights and associated linguistic variables to determine their belonging. The last step of defuzzification was the process of converting the fuzzy output to a crisp value using an inference engine. The operation of the proposed system was implemented using the example of a patient where the developed system specified the severity of his disease. The proposed fuzzy logistic system provides a reasonably fast diagnostic method and can serve as a confirmation of the primary diagnosis of a specialized doctor.
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2. Zadeh LA. Towards a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets and Systems. 1997; 19(2): 111–127.
3. Zadeh LA. Fuzzy sets as a basis for a theory of possibility. Fuzzy sets & Systems. 1978; 1(1): 3–28.
4. Onuwa A B. Fuzzy Expert System For Malaria Diagnosis. Oriental Journal of Computer Science and Technology. 2014; 7(2). Available at: http://www.computerscijournal.org/?p=1084.
5. Djam XY, Wajiga GM, Kimbi YH, Blamah NV. Fuzzy Expert System for the Management of Malaria. International Journal of Pure and Applied Sciences and Technology. 2011; 5(2): 84–108.
6. Oscar Takam Nkamgang , Daniel Tchiotsop , Hilaire Bertrand Fotsin , Pierre KisitoTalla , Valérie Louis Dorr, Didier Wolf. Automating the clinical stools exam using image processing integrated in an expert system. Informatics in Medicine Unlocked. 2019; 15: 100165.
7. Fatumo SA, Emmanuel A, Onaolapo JO. Implementation of XpertMalTyph : an expert system for medical diagnosis of the complications of malaria and typhoid. Journal of Computer Engineering (IOSRJCE). 2013; 8 (5): 34–40.
8. Nkuma-Udah KI, Chukwudebe GA. Medical diagnosis expert system for malaria and related diseases for developing countries. IEEE, 3rd international conference on electro-technology for national development (NIGERCON (2017): 24–29.
9. Saha TB, Daniel T, Valérie LD, Didier W. Towards an automated medical diagnosis system for intestinal parasitosis. Informatics in Medicine Unlocked. 2018. doi : 10.1016/j.imu.2018.09.004.
10. https://speakingofmedicine.plos.org/2015/01/16/one-million-deaths-parasites/
11. https://www.cdc.gov/parasites/crypto/index.html
12. https://p-87.ru/health/enterobioz/
13. Imianvan AA, Anosike UF, Obi JC. An Expert System for the Intelligent Diagnosis of Hiv Using Fuzzy Cluster Means Algorithm. Global Journal of Computer Science and Technology. 2011; 11(12). Version 1.0.
14. Lotfi A. Zadeh. Fuzzy Logic, Neural Networks, and Soft Computing. Communication of the ACM. 1994; 37(3): 77–83.
For citation
Abdullaev N. T., Pashayeva K. Sh., Musevi U. N. Fuzzy Logic system for improving the accuracy of diagnosing parasitic diseases of the gastrointestinal tract. Medical doctor and information technology. 2021; 1: 63–74. (In Russ.). doi : 10.25881/ITP.2021.96.89.006.
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