Fuzzy-evolutionary synergism in an intelligent medical diagnosis system

Abstract

In this paper, we present the design, implementation and evaluation of HIGAS, a hybrid intelligent system that deals with diagnosis and treatment consultation of acid-base disturbances based on blood gas analysis data. The system mainly consists of a fuzzy expert system that incorporates an evolutionary algorithm in an off-line mode. The diagnosis process, the input variables and their values were modeled based on expert's knowledge and existing literature. The fuzzy rules are organized in groups to be able to simulate the diagnosis process. Differential evolution algorithm is used to fine-tune the membership functions of the fuzzy variables. Medium scale experimental results show that HIGAS does better than its non-hybrid version, non-experts and other previous computer-based approaches. © Springer-Verlag Berlin Heidelberg 2006.