EXTRACTION OF RULES FOR TUBERCULOSIS DIAGNOSIS USING AN ARTIFICIAL NEURAL-NETWORK

The treatment of tuberculosis (TB) is a major challenge throughout the world. The Western Cape Region of South Africa has the highest occurr ence of TB in the world. Here, TB is increasing due to improperly mana ged treatment programmes and inadequate facilities. The development of rules to aid medical practitioners in the early and accurate diagnosi s of tuberculosis should prove worthwhile. A method to extract such di agnostic rules from an artificial neural network is presented. These r ules accurately represent the knowledge embedded in the ''raw'' TB dat a.