چکیده (انگلیسی):

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.