Title :
New hybrid hepatitis diagnosis system based on Genetic algorithm and adaptive network fuzzy inference system
Author :
Adeli, M. ; Bigdeli, Nooshin ; Afshar, Karim
Author_Institution :
Imam Khomeini Int. Univ., Ghazvin, Iran
Abstract :
In this paper, a hybrid method for diagnosing hepatitis diseases is introduced. The proposed method consists of two stages: the feature selection and the classification. The feature selection has been performed by Genetic algorithm (GA) as a fast and common intelligent method for feature selection to reduce the number of employed features. For the classification, a major intelligent classification method, Adaptive Network Fuzzy Inference System (ANFIS), is employed. In this way, a hybrid method of GA-ANFIS is developed and evaluated via a set of experimental data. The results are representative of the out-performance the proposed methods with respect to other methods in the literature considering the classification accuracy as the comparison tool.
Keywords :
diseases; feature extraction; fuzzy reasoning; genetic algorithms; medical diagnostic computing; patient diagnosis; pattern classification; GA-ANFIS hybrid method; adaptive network fuzzy inference system; feature selection; genetic algorithm; hepatitis disease diagnosis; hybrid hepatitis diagnosis system; intelligent classification method; Accuracy; Biological cells; Databases; Diseases; Genetic algorithms; Sociology; Statistics; Adaptive Network Fuzzy Inference System (ANFIS); Classification; Feature selection; Genetic algorithm (GA);
Conference_Titel :
Electrical Engineering (ICEE), 2013 21st Iranian Conference on
Conference_Location :
Mashhad
DOI :
10.1109/IranianCEE.2013.6599872