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The FuzzyFluid application was designed to be an expert system using fuzzy logic and fuzzy functions to interpret cerebrospinal fluid data (cytology, glucose concentration and protein levels). Fuzzy logic is based on fuzzy set theory and is the basis of fuzzy modelling. Fuzzy set theory allows objects to have partial membership in sets. Thus fuzzy logic is not bivalent - i.e. confined to only two values (true or false) - but is based on the proposition that an object can have many values, and can therefore describe approximate relationships. Fuzzy logic can be used to construct more complex models than bivalent logic. Fuzzy logic and fuzzy sets were first described in 1965 by Professor Lotfi Zadeh of the University of California (1). Zadeh pointed out that as a model grows more complex, the need to define the model precisely diminishes. The operation and controls of a system can be described linguistically and linguistic expressions can be used in the design of measurement algorithms. This imprecision works in the same way as people do when they solve practical problems. Fuzzy logic replaces inflexible bivalent logic and complicated mathematical functions with simple linguistic rules. The models based on these rules can be constructed using relatively little mathematics, and exhaustive computer calculation can be replaced with easily manageable computer programs. Fuzzy logic can actually be used to create models that cannot be constructed using other methods. The purpose of the FuzzyFluid program is to visualise the use of fuzzy logic in an expert system developed to interpret cerebrospinal fluid, which can be used in normal laboratory procedures, especially in the field of pathology. The program requires information about cytological findings as well as data on glucose concentration and protein levels.

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