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.
