PCA-ANN for Classification of Hepatitis-C Patients

In this paper, an automatic diagnosis system based on Neural Network for hepatitis virus is introduced. This automatic diagnosis system deals with the mixture of feature extraction and classification. The system has two stages, which are feature extraction – reduction and classification stages. In the feature extraction – diminution stage, the hepatitis features were obtained from UCI Repository of Machine Learning Databases. Missing values of the instances are adjusted using local mean method. Then, the number of these features was reduced to 6 from 19 due to relative significance of fields. In the classification stage, these reduced features are given as inputs Neural Network classifier. The classification accuracy of this ANN diagnosis system for the diagnosis of hepatitis virus was obtained, this accuracy was around 99.1% for training data and 100% for testing data. General Terms Data Mining

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