Classification of Acute Leukemia using Fuzzy Neural Networks

Abstract

Accurate classification of cancers based on microarray gene expressions is very important for doctors to choose a proper treatment. In this paper, we compared ten filter based gene selection methods in order to differentiate acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) in leukemia dataset. Dimensionality reduction methods, such as Spearman Correlation Coefficient and Wilcoxon Rank Sum Statistics are used for gene selection. The experimental results showed that the proposed gene selection methods are efficient, effective, and robust in identifying differentially expressed genes. Adopting the existing SVM-based and KNN-based classifiers, the selected genes by filter based methods in general give more accurate classification results, typically when the sample class sizes in the training dataset are unbalanced.