Classification the Results of Angiography by Using Adaptive Nero-Fuzzy Inference System and Genetic Algorithm

Early detection of coronary artery disease is critical. Previous studies show that new methods of artificial intelligence for prediction and diagnosis of diseases have been widely considered by the researchers. The purpose of this paper is the classification of angiography to normal and abnormal by using Artificial Intelligent methods.
In this diagnostic study, the datasets were collected from 152 patients who had been undergoing the coronary angiography, and for classification of the angiography results, a combination of fuzzy neural network and genetic algorithm is used. The proposed system was implemented and evaluated by MATLAB software.
In implementation of the proposed system, 85% of the data was used for the training phase and the remaining 15 percent was used for the test phase. The results of the simulation in accuracy, sensitivity and specificity indicators on average state were 0.9496, 0.9253, 0.9435 and 0.9569, respectively. Under optimum conditions, an estimate of 1 was obtained for each indicator.
The use of genetic algorithms in ANFIS training phase led to improved speed of the simulation. Also the high performance indicators prove that the proposed system is effective for classifying and diagnosing patients with coronary heart disease.