Classification of Heart Disease Using Resilient Back Propagation Algorithm

Heart disease diagnosis is a complex taskwhich requires much experience and knowledge.Traditional way of predicting Heart disease isdoctor’s examination or number of medical testssuch as ECG, Stress Test, and Heart MRI etc.Computer based information along with advancedneural network techniques are used forappropriate results. In many application domains,classification of complex measurements isessential in a diagnosis process. Correctclassification of measurements may in fact be themost critical part of the diagnostic process.Neural Networks have emerged as an importanttool for classification. In this system, we intend todetermine whether a patient has heart disease ornot and if we have heart disease what stage is it byusing multilayer feed forward neural network withresilient back propagation algorithm .Experiments were evaluated on some publicdatasets collected from the Cleveland ClinicFoundation in the UCI (University of California,Irvine) machine learning repository in order totest this system