Classification of Heart Disease Using Resilient Back Propagation Algorithm

Abstract

Heart disease diagnosis is a complex task which requires much experience and knowledge. Traditional way of predicting Heart disease is doctor’s examination or number of medical tests such as ECG, Stress Test, and Heart MRI etc. Computer based information along with advanced neural network techniques are used for appropriate results. In many application domains, classification of complex measurements is essential in a diagnosis process. Correct classification of measurements may in fact be the most critical part of the diagnostic process. Neural Networks have emerged as an important tool for classification. In this system, we intend to determine whether a patient has heart disease or not and if we have heart disease what stage is it by using multilayer feed forward neural network with resilient back propagation algorithm . Experiments were evaluated on some public datasets collected from the Cleveland Clinic Foundation in the UCI (University of California, Irvine) machine learning repository in order to test this system.