This paper proposes an adaptive framework for a Knowledge Based Intelligent Clinical Decision Support System for the prediction of hepatitis B which is one of the most deadly viral infections that has a monumental effect on the health of people afflicted with it and has for long remained a perennial health problem affecting a significant number of people the world over. In the framework the patient information is fed into the system; the Knowledge base stores all the information to be used by the Clinical Decision Support System and the classification/prediction algorithm chosen after a thorough evaluation of relevant classification algorithms for this work is the C4.5 Decision Tree Algorithm with its percentage of correctly classified instances given as 61.0734%; it searches the Knowledge base recursively and matches the patient information with the pertinent rules that suit each case and thereafter gives the most precise prediction as to whether the patient is prone to hepatitis B or not. This approach to the prediction of hepatitis B provides a very potent solution to the problem of determining if a person has the likelihood of developing this dreaded illness or is almost not susceptible to the ailment.