A KNOWLEDGE-BASED SYSTEM USING MULTIPLE EXPERT MODULES FOR MONITORINGLEPROSY - AN ENDEMIC DISEASE

An environment with multiple expert modules is essential for proper ha ndling of diagnosis and monitoring of chronic endemic diseases. In thi s paper, we present LEPDIAG-a knowledge based system for diagnosis and monitoring of leprosy. The proposed architecture is a conglomeration of three expert modules and a procedural performance evaluator. A nove l feature of the architecture is inclusion of the homeostatic expert m odule which models the immunological reaction of the patient. The enti re system provides a closed loop diagnosis and follow-up environment. LEPDIAG is built around the fuzzy expert system building tool FRUIT fo r dealing with imprecise knowledge. The domain knowledge in LEPDIAG is expressed by fuzzy production rules. The rules have been partitioned using suitable clustering criteria. The rule conflict is resolved usin g metarules. The information objects used and the fuzzy inference stra tegy adopted have been illustrated.