Determination of hepatotropic virus in human metabolism using artificial neural networks

Author

Ansari, Sana ; Shafi, Imran ; Ahmad, Jamil ; Shah, Syed Ismail

Author_Institution

Dept. of Comput. & Technol., Iqra Univ., Islamabad, Pakistan

fYear

2010

fDate

18-19 Oct. 2010

Firstpage

11

Lastpage

15

Abstract

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.

Keywords

backpropagation; diseases; liver; medical computing; microorganisms; patient diagnosis; radial basis function networks; artificial neural network; feed forward back propagation; generalized regression neural network; hepatotropic virus determination; human metabolism; liver function test; patient diagnosis; Artificial neural networks; Cancer; Diseases; Liver; Medical diagnostic imaging; Neurons; Training; Artificial neural network; Cirrhosis diagnosis; Feed forward back propagation; Generalized regression; Radial basis function;

fLanguage

English

Publisher

ieee

Conference_Titel

Emerging Technologies (ICET), 2010 6th International Conference on

Conference_Location

Islamabad

Print_ISBN

978-1-4244-8057-9

Type

conf

DOI

10.1109/ICET.2010.5638390

Filename

5638390