Abstract
In this paper, we study the matrix representation of fuzzy soft sets, complement of fuzzy soft sets, product of fuzzy soft matrices and the application of fuzzy soft matrices in medical diagnosis presented by Lavanya and Akila. Additionally, a new method (max-min average) based on fuzzy reference function is introduced instead of the max-product method by Lavanya and Akila to extend Sanchez’s technique for decision making problems in medical diagnosis. Using the same data by Lavanya and Akila, the result shows that the new method gives more information about the medical status of the patients being considered in relation to a set of diseases.
Highlights
Zadeh [1] was the first to introduce the theory of fuzzy sets (FS)
ZT1 − ZT2 = 0 and the person pi is attacked by the disease dj, but the person is healthy enough to suppress the effect of the disease
We established the new method proposed based on the fuzzy reference function is more efficient, in the sense that it gives more information about the health status of a particular patient pi in relation to suffering a disease dj, when compared with Lavanya and Akila [37]
Summary
Zadeh [1] was the first to introduce the theory of fuzzy sets (FS). In 1999, Molodtsov [2]. Neog and Sut [23,24] re-presented the definition of FSS, fuzzy soft complement matrix and put forward FSM and its applications. We study the Lavanya and Akila’s technique of medical diagnosis using fuzzy soft complement matrix initiated by Neog and Sut [23]. The limitation with these earlier techniques is that they only point out the extent to which an attribute is exhibited. An application of FSM using a revised method based on fuzzy reference function to extend Sanchez’s technique for decision making problems in medical diagnosis is presented. A revision of it is made and a new method for fuzzy soft matrices based on the reference function is presented
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