KNOWLEDGE BASED SYSTEM DESIGN FOR DIAGNOSIS OF HEPATITIS B VIRUS (HBV) USING GENERALIZED REGRESSION NEURAL NETWORK (GRNN)
DOI:
https://doi.org/10.47672/ajce.270Keywords:
Diagnosis, Hyper-Severity, Knowledge Based System, Generalized Regression Neural Network (GRNN), Hepatitis B Virus (HBV).Abstract
Purpose: It is obvious that accurate diagnosis of a disease is one of the serious problems in modern medicine. This paper proposes a knowledge base system design for the diagnosis of Hepatitis B virus (HBV) using Generalized Regression Neural Network (GRNN). The aim is to embed an intelligent system for the diagnosis of Hepatitis B virus using GRNN since HBV is one of the most deadly viral infections that has colossal effect on the health of the people suffering from it and has remained a lasting health problem affecting a significant number of the world's population.
Methodology: The data used for this study was obtained from different sources. Primary data was obtained from field through, observations, and scheduled interviews with stakeholders -Medical Doctors Laboratory Technicians, Laboratory Scientists and Patients suffering from the disease. While secondary data was gotten through visits to the libraries, journals, textbooks, articles and conference proceedings.
Results: Hepatitis B is one of the most common of all Hepatitis around the world today. The research found out that using the HBV markers that, if AgHBs = positive, AgHBe = positive and anti-VHD = negative then HBV is Positive, if HBsAg = negative, anti-HBc = positive, IgM anti-HBc = positive and anti-HBs = positive then it is at Acute level, if HBsAg = positive, anti-HBc = positive, IgM anti-HBc = negative and anti-HBs = negative then it is Chronic. Finally, if HBeAg = positive then the Liver is inflammed (HBV profile test). Generalized regression Neural Network (GRNN) is the finest suitable Neural Network for Hepatitis B diagnosis which will help in reducing extra time consumption in treatment. Even if there is any number of missing parameters in blood test, the diagnosis will be done by artificial intelligence using generalized regression neural networks.
Unique contribution to theory, practice and policy: This system will help assist the health practitioners and also keep the vulnerable informed, as well the mortality rate and waiting time to see the experts will be reduced by employing the expert system application in this research. The researcher here recommend for further study on HBV drug resistance.
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