Decision Support System for Hepatitis Disease Diagnosis using Bayesian Network

Abstract

Medical judgments are tough and challenging as the decisions are often based on deficient and ambiguous information. Moreover the result of decision process has direct effects on human lives. Act of human decision declines in emergency situations due to complication, time limit and high risks. Therefore, provision of medical diagnosis plays a dynamic role, specifically in preliminary stage when a physician has limited diagnosis experience and identifies the directions to be taken for the treatment process. Computerized Decision Support Systems have brought a revolution in the medical diagnosis. These automatic systems support the diagnosticians in the course of diagnosis. The major role of Decision Support Systems is to support the medical personnel in decision making procedures regarding disease diagnosis and treatment recommendation. The proposed system provides easy support in Hepatitis disease recognition. The system is developed using the Bayesian network model. The physician provides the input to the system in the form of symptoms stated by patient. These signs and symptoms match with the casual relationships present in the knowledge model. The Bayesian network infer conclusion from the knowledge model and calculate the probability of occurrence of Hepatitis B, C and D disorders.