  The goal is to develop a novel approach for cardiac disease prediction and
diagnosis using intelligent agents. Initially the symptoms are preprocessed
using filter and wrapper based agents. The filter removes the missing or
irrelevant symptoms. Wrapper is used to extract the data in the data set
according to the threshold limits. Dependency of each symptom is identified
using dependency checker agent. The classification is based on the prior and
posterior probability of the symptoms with the evidence value. Finally the
symptoms are classified in to five classes namely absence, starting, mild,
moderate and serious. Using the cooperative approach the cardiac problem is
solved and verified.
