  We develop a neural network model to classify liver cancer patients into
high-risk and low-risk groups using genomic data. Our approach provides a novel
technique to classify big data sets using neural network models. We preprocess
the data before training the neural network models. We first expand the data
using wavelet analysis. We then compress the wavelet coefficients by mapping
them onto a new scaled orthonormal coordinate system. Then the data is used to
train a neural network model that enables us to classify cancer patients into
two different classes of high-risk and low-risk patients. We use the
leave-one-out approach to build a neural network model. This neural network
model enables us to classify a patient using genomic data as a high-risk or
low-risk patient without any information about the survival time of the
patient. The results from genomic data analysis are compared with survival time
analysis. It is shown that the expansion and compression of data using wavelet
analysis and singular value decomposition (SVD) is essential to train the
neural network model.
