Identification of Heart Disease With Iridology Using Back propagation Neural Network

Heart is an organ which function is to pump blood throughout the whole body. Because of it’s never-ending work, heart is prone to have problems. Which is why a simpler and more efficient method to identify and recognize heart complication is needed. By using iridology, heart complication can be recognized through the iris. This research was done to create a system which can recognize a problem that is happening on the heart by observing the iris. The system will feature extraction by using two methods, Principal Component Analysis (PCA) and Gray Level Co-occurrence Matrix (GLCM), these are done to identify the effects of feature extraction method against the success rate and classification using Backpropagation Neural Network. The result showed that the success rate of using PCA produced 90% and using GLCM was 77.5%.