A new approach based on PCA and CE-SVM for hepatitis diagnosis

Abstract

In this study, we propose a new approach that aims improving the medical diagnosis of hepatitis disease via Machine Learning techniques. The proposed solution consists of two stages. In the first one, Principal Component Analysis (PCA) is used to reduce the dimension of features vector. In the second one, classification process is implemented using the remaining features through Support Vector Machine (SVM) method which its parameters are determined using Cross Entropy Optimisation (CEO) that represents an efficient stochastic optimization tool. We have performed experiments on UCI datasets with the combination of SVM with CE. Classification accuracy obtained with the proposed approach is very promising with regard to the existing classification methods for hepatitis disease diagnosing.