A Neural Network Model to Classify Liver Cancer Patients Using Data Expansion and Compression

TL;DR

This paper introduces a neural network approach for classifying liver cancer patients into high-risk and low-risk groups using genomic data, employing wavelet analysis and SVD for data preprocessing.

Contribution

It presents a novel data expansion and compression technique with wavelet analysis and SVD to improve neural network classification of big genomic datasets.

Findings

Wavelet analysis enhances data representation.

Data compression via SVD improves model training.

High accuracy in classifying patient risk levels.

Abstract

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.