A Study of Fuzzy Relation and Its Application in Medical Diagnosis
Asian Research Journal of Mathematics,
Objective: Medical diagnosis process extends within the degree to which they plan to affect different complicating aspects of diagnosis. In this research work, the concept of fuzzy relation with medical diagnosis is studied and the application of fuzzy relations to such problems by extending the Sanchez’s approach is introduced.
Method: An application of fuzzy relation with Sanchez's approach for medical diagnosis is presented. Based on the composition of the fuzzy relations, an algorithm for medical diagnosis as follows- first input the number of objects and attributes to obtain patient symptom matrix, symptom-disease matrix and the composition of fuzzy relations to get the patient-diagnosis matrix. Then find the maximum value to evaluate which patient is suffering from what disease.
Result: Using the algorithm for medical diagnosis, the disease for which the membership value is maximum gives the final decision. If almost equal values for different diagnosis in composition are obtained, the case for which non-membership is minimum and hesitation is least is considered. The output matched well with the doctor’s diagnosis.
Conclusion: In the process of medical diagnosis, state of patient are given by the patient through linguistic terminology like as temperature, cough, stomach pain etc., consideration of fuzzy sets as grades for association instead of membership grades in [0,1] is more advantageous to model the state of the patient. Similarly fuzzy relation has been introduced representing the association between symptoms and diseases. Sanchez’s approach has been extended for medical diagnosis in this reference. The approach used to form fuzzy matrix showing the association of symptoms and diseases is based on the sanchez’s approach.
- Fuzzy logic
- medical diagnosis
- membership function
- sanchez’s approach.
How to Cite
Roy MK, Biswas R. I-v fuzzy relations and Sanchez’s approach for medical diagnosis, Fuzzy Sets and Systems. 1992;47:35-38.
Zadeh LA. Fuzzy sets. Information and Control. 1965;8(3):338-353.
Bui Cong Cuong, Pham Hong Phong. Max-min composition of linguistic intuitionistic fuzzy relations and application in medical diagnosis. VNU Journal of Science: Comp. Science and Com. Eng. 2014;30(4):57-65.
Muhammad Naveed Jafar, Kainat Munib, Ayesha Saeedet, et al. Application of sanchez’s approach to disease identification using trapezoidal fuzzy numbers. International Journal of Latest Engineering Research and Applications. 2019;4(9):5-57.
Atanassov K. Intuitionistic Fuzzy Sets; Springer: Heidelberg, Germany; 1999.
Sanchez E. Medical diagnosis and composite fuzzy relations, In: Gupta MM, Ragde RK, Yager RR. Advances in Fuzzy Set Theory and Applications. North Holland, NewYork. 1976;437-444.
Hussain M. Fuzzy Relations, Mathematical Modelling and Simulation. 2010;10-18.
Atanassov K. On Intuitionistic Fuzzy Sets Theory; Springer: Berlin, Germany; 2012.
Abstract View: 52 times
PDF Download: 32 times