Intelligent Diagnosis of Hepatitis Disease using Union-based Fuzzy Neural Networks

Nowadays fuzzy neural networks have been successfully applied to intelligent diagnosis of many diseases. This paper applies union-based fuzzy neural networks to intelligent diagnosis of hepatitis disease that is very common in the world and needs to be diagnosed exactly. Union-based fuzzy neural networks can guarantee a reduced knowledge base with subset of all possible rules by allowing union in the rule antecedent. Genetic algorithms optimize the binary connections of the union-based rule antecedent fuzzy neural networks, and then gradient-based learning refines the optimized binary connections in the unit interval. To show the applicability of the proposed method, we consider the hepatitis disease dataset available on the Machine Learning Repository site at the University of California at Irvine.