Fuzzy Relational Equation in Preventing Diabetic Heart Attack

Data mining aims at discovering knowledge out of data and presenting it in a form that is easily compressible to humans. It is a process that is developed to examine large amounts of data routinely collected. Fuzzy systems are been used for solving a wide range of problems in different application domain genetic algorithm for designing. Fuzzy systems allows us to introduce the learning and adaptation capabilities. The fuzzy set framework has been used in several different process of diagnosis of disease. Fuzzy logic is a computational paradigm that provides a mathematical tool for dealing with the uncertainty and the imprecision typical of human reasoning. Fuzzy relational between symptoms and risks factors for diabetic based on the expert´s medical knowledge is taken and also related complications or due to some common metabolic disorder it may lead to vision loss, heart failure, stroke, foot ulcer, nerves. In this paper the fuzzy set A is taken as symptoms observed in the patient and fuzzy relation R representing the medical knowledge that relates the symptoms in set S to the diseases in set D, then the fuzzy set B of the possible diseases of the patients can be inferred by means of the compositional rule of inference. Neural Networks are efficiently used for learning membership functions, fuzzy inference rules and other context dependent patterns; fuzzification of neural networks extends their capabilities in applicability. First experts detection is only based on patients articulate that is compared by medical knowledge, that may lead to various modifications and due to patients rejections of certain symptoms may be inappropriate. The proposed detection system uses one committee of multilayer Perceptron Neural Networks (MLP) for each one of the entity. Using back propagation algorithm the multilayer perceptron works again and again to remove errors in the network.