A fuzzy set of is a function.
Kaufmann [2] first introduced fuzzy variable as a fuzzy set of real numbers to describe fuzzy phenomena.
Fuzzy subsethood of A in B is the degree to which fuzzy set as point A belongs to fuzzy set as point B. The fuzzy measure of comparison F Comp ( A, B ) = F Sim ( A,B ) – (K-1), where K is the fuzzy measure of symmetry breaking of conditions previously defined by us from the fuzzy subsethood theorem and measured in fuzzy cardinality.
The measures K and 1/K characterize the transformation of fuzzy set as point A to fuzzy set as point B in the unit hypercube.
Let (E^{n}) denote the collection of all fuzzy sets of (R^{n}) that satisfies the conditions, i.e., u is normal, fuzzy convex, upper semicontinuous and ([u]^{0}) is compact.
Let denote the collection of all fuzzy sets of that satisfies the following conditions: (1) is normal, that is, there exists an such that ; (2) is fuzzy convex, that is, for any, ; (3) is upper semicontinuous, that is, for any, ; (4) is compact. . is normal, that is, there exists an such that ; is fuzzy convex, that is, for any, ; is upper semicontinuous, that is, for any, ; is compact.
Regarding fuzzy set arithmetic, α-cuts of fuzzy sets are always closed and bounded intervals.
After the fuzzy sets of each numerical feature are obtained, the corresponding membership function can be generated for each fuzzy set.
In figure 11.a the domain is divided into six fuzzy sets of equal size.
The derived fuzzy sets of input numerical patient features are shown in Figure 3.
