Determination of hepatotropic virus in human metabolism using artificial neural networks

This paper proposes an artificial neural network (ANN) based approach to diagnose patients infected with hepatotropic virus and the stage of disease. The proposed method detects the disease and classifies its stage to be acute, chronic or cirrhosis. The input to the system is in the form of basic pathological data based on various liver function tests (LFTs) and specific virological markers. In addition, the paper compares the performance of feed forward back propagation (FFNN) and generalized regression (radial basis) neural network (GRNN) for the subject task. It is concluded that the FFNN performs better than the GRNN even with a small data set.