Background: Artificial Neural Networks (ANNs) can be used to classify tumor of Hepatocellular
carcinoma based on their gene expression signatures. The neural network is trained with gene
expression profiles of genes that were predictive of recurrence in liver cancer, the ANNs became capable
of correctly classifying all samples and distinguishing the genes most suitable for the organization.
The ability of the trained ANN models in recognizing the Cancer Genes was tested as we analyzed
additional samples that were not used beforehand for the training procedure, and got the correctly
classified result in the validation set. Bootstrapping of training and analysis of dataset was made
as external justification for more substantial result.
Result: The best result achieved when the number of hidden layers was 10. The R2 value with training
is 0.99136, R2 value obtained with testing is 0.80515, R2 value obtained after validation is 0.76678
and finally, with the total number of sets the R2 value is 0.93417. Performance was reported on the basis
of graph plotted between Mean Squared error (MSE) and 23 epoch. The value of gradient of the
curve was 152 after 6 validation checks and 23 iterations.
Conclusion: A successful attempt at developing a method for diagnostic classification of tumors from
their gene-expression autographs that efficiently classify tumors and helps in decision making for providing
appropriate treatment to the patients suffering from Hepatocellular carcinoma has been carried