@article {10.3844/jcssp.2014.563.569,
article_type = {journal},
title = {GENERALIZED REGRESSION NEURAL NETWORK BASED EXPERT SYSTEM FOR HEPATITIS B DIAGNOSIS},
author = {Mahesh, C. and Kannan, E. and Saravanan, M. S.},
volume = {10},
number = {4},
year = {2013},
month = {Dec},
pages = {563-569},
doi = {10.3844/jcssp.2014.563.569},
url = {https://thescipub.com/abstract/jcssp.2014.563.569},
abstract = {Hepatitis B is a potentially life-threatening liver infection caused by the hepatitis B virus. The virus interferes with the function of the liver while replicating in hepatocytes. It is a major global health problem and the most serious type of viral hepatitis. Chronic liver disease is caused by viral hepatitis and putting people at high risk of death from cirrhosis of the liver and liver cancer. Medical information available is extensive and which is utilized by the clinical specialists. The ranging of information is from details of clinical symptoms to various types of biochemical data. Information provided by each data is evaluated and assigned to a particular pathology during the diagnostic process. Artificial intelligence methods especially computer aided diagnosis and artificial neural networks can be employed to streamline the diagnostic process. These adaptive learning algorithms can handle diverse types of medical data and integrate them into categorized outputs. Artificial neural networks are finding many uses in the medical diagnosis application. In this study we have proposed a Generalized Regression Neural Network (GRNN) based expert system for the diagnosis of the hepatitis B virus disease. The system classifies each patient into infected and non-infected. If infected then how severe it is in terms of intensity rate.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}