Mining Classification Rules for Liver Disorders

Nowadays data mining is a very popular technique and has been successfully applied in medical area. Classification is a essential approach in data mining. One of the classification methods is a Artificial Neural Networks. Artificial Neural Network (ANN) generally achieve high accuracy of classification. However, knowledge acquired by ANN is incomprehensible for humans. This fact is causing a serious problem in data mining applications. The rules that are derived from ANN are needed to be formed to solve this problem and various methods have been improved to extract these rules. Selection of the activation function is important in the performance of ANN. Networks with adaptive activation function seem to provide better fitting properties than classical architectures with fixed activation function neurons [1]. In this study, first neural network has been trained with adaptive activation function. Then for the purpose of extracting rules from adaptive ANN which has been trained for classification, OptaiNET that is an Artificial Immune Algorithm (AIS) has been used and a set of rules has been formed for liver disorder.