Diagnosis of Coronary Artery Disease Using Artificial Intelligence Based
  Decision Support System
  This research is about the development a fuzzy decision support system for
the diagnosis of coronary artery disease based on evidence. The coronary artery
disease data sets taken from University California Irvine (UCI) are used. The
knowledge base of fuzzy decision support system is taken by using rules
extraction method based on Rough Set Theory. The rules then are selected and
fuzzified based on information from discretization of numerical attributes.
Fuzzy rules weight is proposed using the information from support of extracted
rules. UCI heart disease data sets collected from U.S., Switzerland and
Hungary, data from Ipoh Specialist Hospital Malaysia are used to verify the
proposed system. The results show that the system is able to give the
percentage of coronary artery blocking better than cardiologists and
angiography. The results of the proposed system were verified and validated by
three expert cardiologists and are considered to be more efficient and useful.
