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 offers an intelligent Hepatitis disease diagnosis system to provide ease and support in Hepatitis disease recognition. The system is developed using the probabilistic model Bayesian network. 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 infers conclusion from the knowledge model and calculates the probability of occurrence of Hepatitis B, C and D disorders. The Bayesian network also classifies the categories of Hepatitis disorder and ranked them based on the probability values. Consequently the particular category is diagnosed from which the patient is infected. The results of the proposed system show prominent and could assist the physicians in the diagnosis process of hepatitis disease.

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