Adaptive neurofuzzy system for tuberculosis

In this paper, a neurofuzzy system for tuberculosis (TB) is presented. This proposed work is rule-based fuzzy system which is form of intelligent technique and contain symptoms as its input variables in certain specified ranges & possible cures or referrals to doctors as its output. The adaptability of proposed work is depending upon the rule based algorithm which has decision-making ability and backpropagation learning of neurofuzzy system. Simulated results show the proposed work for automated diagnosis, which have performed by using the realistic causes of tuberculosis disease are effective.