MICROARRAY DATA CLASSIFICATION USING DUAL TREE M-BAND WAVELET FEATURES

Abstract

Deoxyribo Nucleic Acid (DNA) microarrays are widely used to monitor the expression levels of genes in parallel. It is possible to predict human cancer using the expression levels from a collection of DNA samples. Due to the vast number of genes expression level, it is challenging to analyze them manually. In this paper, data mining approach is used to extract the prevailing information from DNA microarray with the help of multiresolution analysis tool. Dual Tree M-Band Wavelet Transform (DTMBWT) is employed for the extraction of features from the given dataset at the 2nd level of decomposition. K-Nearest Neighbor (KNN) classifier is used for cancer classification. Results show that KNN classifier classifies five different cancer datasets; Breast, Colon, Ovarian, CNS, and Leukemia with over 90% accuracy.