A comparison of methodologies for fuzzy expert system creation--application to arrhythmic beat classification.
In this work, three different methodologies for fuzzy expert systems creation are compared: a well-known neuro-fuzzy approach, a knowledge-based approach and a novel methodology, based on rule-extraction. The adaptive neuro-fuzzy information system (ANFIS) is used to automatically generate a fuzzy expert system. In the knowledge-based approach and the rule-extraction methodology, the idea is to start with a model described by crisp rules, provided by medical experts in the first case or extracted using data mining techniques in the second, and then to transform them into a set of fuzzy rules, creating a fuzzy model. In either case, the adjustment of the model's parameters is performed via a stochastic global optimization procedure. All three approaches are applied to a medical domain problem, the cardiac arrhythmic beat classification. The ability to interpret the decisions made from the created fuzzy expert systems is a major advantage compared to other "black box" approaches.
Tsipouras
Markos G
MG
Unit of Medical Technology and Intelligent Information Systems, Department of Computer Science, University of Ioannina, Greece. markos@cs.uoi.gr
Exarchos
Themis P
TP
Fotiadis
Dimitrios I
DI
D000465:Algorithms; D001145:Arrhythmias, Cardiac / Q000145:classification / Q000175:diagnosis* / Q000503:physiopathology*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004562:Electrocardiography / Q000379:methods*; D005103:Expert Systems; D017143:Fuzzy Logic; D006801:Humans; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity