Diagnosis of heart disease and hyperacidity of stomach through iridology based on the neural network

Abstract

The main objective of this study is to evaluate the disease diagnosis through iridology by neural network that uses eye structure and a mechanism to diagnose accurately the iris position and matching with iridology pattern evaluating the accurate positions. So, the offered software evaluates accurately iris tissue through edge detection and then analyzes the intended area according to the disease type and finally by the neural network, evaluates learning and prediction in health area. The risks of a lack of accurate and rapid disease detection with a high cost of diagnosis is considered as one of the problems of medical society. Creation of a mechanism to manage the volume of patient information and effective use of them to improve decision-making is one the controversial topics in this era. One of the important issues in computer science research, is to implement a similar model to the internal system of the human brain to analyze different systems based on the experience. In this regard, neural networks are one of the most dynamic areas of research in this era and using neural networks to solve the applicable complicated problems has increased these days. The architecture of the offered model contains 4 main sections of receiver block, processing block, matching block and decision block that this section detects based on a pattern or disease symptoms in the region to accept or reject