Fuzzy reasoning applied to multistage diagnosis of acute renal failure in children

The paper deals with fuzzy inference systems for multistage recognition based on a decision tree scheme. Two conceptually different fuzzy methods are presented and discussed for the given learning set. The first method is developed according to the multistage approach known as the Mamdani inference engine, with rules generated from the learning set. In the second approach, we first construct a fuzzy relation between the decision set and the feature space, which is then used for decision making. Both methods were practically applied to computer-aided medical diagnosis of acute renal failure. Results of comparative experimental analysis are given.