Application of Complex Fuzzy Relational Compositions to Medical Diagnosis

Muhammad Gulzar, Samina Ashraf and Etienne E. Kerre ()
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Muhammad Gulzar: Division of Science and Technology, Department of Mathematics, University of Education, Lahore 54590, Pakistan
Samina Ashraf: Department of Mathematics, University of Education, Bank Road Campus, Lahore 54590, Pakistan
Etienne E. Kerre: Department of Applied Mathematics, Computer Science and Statistics, Ghent University, B-9000 Ghent, Belgium

Mathematics, 2024, vol. 12, issue 23, 1-15

Abstract: The capability of complex fuzzy sets plays a valuable role in resolving many real-life problems. In this paper, we present the compositions of complex fuzzy relations by using the idea of implication operators and max-product compositions of complex fuzzy relations and illustrate these compositions with concrete examples. The converse of these newly invented triangular compositions in terms of compositions of the converse relations is also defined. We also study the interactions with the union and intersection. The main goal of this article is to present a new technique to enhance medical diagnostic models that can assist in improving the features of healthcare systems. We utilize these compositions to diagnose diseases in patients on the basis of the intensity of symptoms.

Keywords: complex fuzzy set; complex fuzzy implication; complex fuzzy relations; complex fuzzy compositions (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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