Representing uncertainty in medical knowledge : an interval based approach for binary fuzzy relations

Abstract EN

This paper addresses issues involved in representation of causal relationships between medical categories.

An interval based approach for medical binary fuzzy relations is proposed to represent the ignorance about uncertainty and imprecision.

A major advancement propagated by this model lies in formalizing some novel medical measures enhancing the sight in understanding the causality relationship between medical entities.

This view is expressed in extension of the classical fuzzy implication relationship in terms of interval valued fuzzy inclusion relationship in the context of fuzzy binary relationships.

The focus of attention of this model is based on utilizing interval based fuzzy inclusion relationships as causality measures expressing the strength of the degree of inclusion between fuzzy sets.

In addition, derived from the direction of an inclusion degree, an interval based causal relationship can medically be interpreted as the necessity or the sufficiency of occurrence of a medical entity such as symptoms or disease with another one.

Furthermore, for simplification of computations and defuzzification of dependent intervals a method for transformation of these relations into point-valued relations is proposed.