A knowledge based system using multiple expert modules for monitoring leprosy-an endemic disease

An environment with multiple expert modules is essential for proper handling of diagnosis and monitoring of chronic endemic diseases. In this 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 novel feature of the architecture is inclusion of the homeostatic expert module which models the immunological reaction of the patient. The entire system provides a closed loop diagnosis and follow-up environment. LEPDIAG is built around the fuzzy expert system building tool FRUIT for 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 using metarules. The information objects used and the fuzzy inference strategy adopted have been illustrated