Medical decision base self-organization system under uncertainty

The object of this study is to present a holonic medical diagnosis system, which unifies the advantages of decision theory under uncertainty with the efficiency, reliability, extensibility, and flexibility of the holonic multi agent system holonic paradigm. This paper also handles an important assumption in Baye's theorem. Clustering and discriminating provide method for solving dependence in symptoms problem. It builds on degree of dependency between symptoms with consequence of raising the efficiency and accuracy of the diagnosis. The idea is to transform raw symptoms of each disease into independent groups. The presented approach focuses on reaching the optimal medical diagnosis with the minimum risk under the given uncertainty. Additional factors that play an important role are the required time for the decision process and the produced costs.