Hepatitis B Diagnosis Using Logical Inference and Self-Organizing Map

Abstract

Despite all the standardization efforts made, medical diagnosis is still regarded as an art owing to the fact that that medical diagnosis requires an expertise in handling the uncertainty which is unavailable in today's computing machinery. Though artificial intelligence is not a new concept it has been widely recognized as a new technology in computer science. Numerous areas such as education, business, medical and manufacturing have made use of artificial intelligence. Problem statement: The proposed study investigated the potential of artificial intelligence techniques principally for medical applications. Neural network algorithms could possible provide an enhanced solution for medical problems. This study analyzed the application of artificial intelligence in conventional hepatitis B diagnosis. Approach: In this research, an intelligent system that worked on basis of logical inference utilized to make a decision on the type of hepatitis that is likely to appear for a patient, if it is hepatitis B or not. Then kohonen's self-organizing map network was applied to hepatitis data for predictions regarding the Hepatitis B which gives severity level on the patient. Results: SOM which is a class of unsupervised network was used as a classifier to predict the accuracy of Hepatitis B. Conclusion: We concluded that the proposed model gives faster and more accurate prediction of hepatitis B and it works as promising tool for predicting of routine hepatitis B from the clinical laboratory data.