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

This paper addresses issues involved in representation of causal relationships between medical categories. Aninterval based approach for medical binary fuzzy relations is proposed to represent the ignorance about uncertainty andimprecision. A major advancement propagated by this model lies in formalizing some novel medical measures enhancing thesight in understanding the causality relationship between medical entities. This view is expressed in extension of the classicalfuzzy implication relationship in terms of interval valued fuzzy inclusion relationship in the context of fuzzy binaryrelationships. The focus of attention of this model is based on utilizing interval based fuzzy inclusion relationships as causalitymeasures expressing the strength of the degree of inclusion between fuzzy sets. In addition, derived from the direction of aninclusion degree, an interval based causal relationship can medically be interpreted as the necessity or the sufficiency ofoccurrence of a medical entity such as symptoms or disease with another one. Furthermore, for simplification of computationsand defuzzification of dependent intervals a method for transformation of these relations into point-valued relations is proposed