Genetic Tuning of Fuzzy Rule Deep Structures for Efficient Knowledge Extraction from Medical Data
Alessandro G. Di Nuovo, Vincenzo Catania · 2006
In medical diagnosis, a correct disease classification is needed to choose the right treatment and to assure a quality of life that is suitable for a patient's condition. In order to meet this need we researched a technique that allows us to perform automatic diagnoses efficiently and reliably and at the same time is easy for practitioners to use. In this paper we present an efficient computational intelligence technique that integrates fuzzy logic and genetic algorithms in order to discover a transparent fuzzy rule based diagnostic system from data. To improve precision without losses in readability we propose the use of linguistic hedges. The approach has been applied to three real-world benchmarks and compared with related works, showing its effectiveness.