Improved local fisher discriminant analysis based dimensionality reduction for cancer disease prediction

Abstract

A good dimensional reduction technique is needed to apply and improve the effectiveness of dimensionality reduction for medical data. High-dimensional data brings great challenges in terms of computational complexity and classification efficiency. It is necessary to compress effectively from high dimensional space to low dimensional space to design a learning curve with good performance. Therefore, dimensional reduction is necessary to study and understand the mechanism of the practical problems of medical data. In this paper, a hybrid local fisher discriminant analysis (HLFDA) method is proposed for the dimension reduction of the medical data. LFDA is a localized variant of Fisher discriminant analysis and it is popular for supervised dimensionality reduction method. The proposed HLFDA is a combination of Locality-preserving projection and LFDA. After the dimensionality reduction process, the data are given to the Type2fuzzy neural network classifier to classify a given data as normal or abnormal. The paper focused on improving performance in terms of prediction accuracy. Three types of UCI cancer dataset is used for analyzing the performance of the proposed method.