Representing Uncertainty in Medical Knowledge: An Interval Based Approach for
Binary Fuzzy Relations

Bassam Haddad and Ahmad Awwad
 Faculty of Infor¬mation Technology, Petra University, Jordan

Abstract: This paper addresses issues involved in representation of causal relationships between medical categories.  An interval based ap¬proach for medical binary fuzzy relations is proposed to represent the ignorance about un¬certainty and impre¬cision. A major advancement propagated by this model lies in formalizing some novel medical measures en¬hancing 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 inclu¬sion degree, an interval based causal relation¬ship can medically be interpreted as the neces¬sity or the sufficiency of occur¬rence of a medical entity such as symptoms or disease with another one.  Furthermore, for sim¬plification of computations and defuzzifi¬cation of dependent intervals a method for transformation of these relations into point-valued relations is pro¬posed.