Computational intelligence for medical knowledge acquisition with application to glaucoma

This paper presents an approach that integrates computational intelligence/soft computing paradigms with clinical investigation methods and knowledge. Computational intelligence methods (including fuzzy logic, neural networks and genetic algorithms) deal in a suitable way with imprecision, uncertainty and partial truth. These aspects can be found quite often in practical medical activities and in medical knowledge. The proposed approach uses a knowledge discovery process in order to develop an intelligent system for diagnosis and prediction of glaucoma. The knowledge acquired is embedded in a fuzzy logic inference system. The resulting neuro-fuzzy glaucoma diagnosis and prediction system is expected to lower the effort, difficulties and risk cost related to this disease (the leading cause of blindness in North America).