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Prediction of Cancer Subtypes using Fuzzy Hypersphere Clustering Neural Network


B. B. M. Krishna Kanth, U. V. Kulkarni, B. G. V. Giridhar


Vol. 11  No. 2  pp. 173-178


The classification of different tumor types is of great importance in cancer diagnosis and drug discovery. However, most previous cancer classification studies are clinical-based and have limited diagnostic ability. Cancer classification using gene expression data is known to contain the keys for addressing the fundamental problems relating to cancer diagnosis and drug discovery. The recent advent of DNA microarray technique has made simultaneous monitoring of thousands of gene expressions possible. We propose a new method of classification system namely, the fuzzy hypersphere clustering neural network (FHCNN) which combines clustering and classification inorder to differentiate cancer tissues such as acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL). Experimental results show that our FHCNN model using one outstanding gene, Zyxin achieves the best classification accuracy of 94.12% where as other state-of-art methods could reach the best accuracy of 91.18%. Moreover FHCNN is more stable, and contains less number of parameter adjustments compared to all the other classification methods.


classification, gene ranking, fuzzy sets, neural network