Diagnosis of Heart Disease using Fuzzy Resolution Mechanism

The aim of this study is to combine the neural networks (ANNs) and Fuzzy Logic (FL) to make a powerful tool to diagnosis heart disease. By combining the Fuzzy inference system and neural network, the input values are passed through the input layer (by input membership function) and the output could be seen in output layer (by output membership functions). Training involves iterative adjustment of parameters of the adaptive neuro-fuzzy inference system using a hybrid learning procedure to diagnosis the heart disease. This mechanism presents five layer, each layer has its own nodes. Layer 1 had the input variables with membership function. T-norm operator that perform the AND operator can be used in layer 2. The sum of all rules firing strengths are assigned in layer 3. The nodes in layer 4 are adaptive and perform the consequent of the rules. Single node computes the overall output in layer 5. The proposed method is tested with Cleveland heart disease dataset. The ANFIS approach is implemented using MATLAB. The proposed mechanism can work more effectively for diagnosis of heart disease and also improves the accuracy. The result of the proposed methods is compared with earlier method using accuracy as metrics.