Diagnosis of Coronary Artery Disease Using Artificial Intelligence Based Decision Support System
This addresses the problem of improving diagnostic accuracy for coronary artery disease in medical practice, though it appears incremental as it builds on existing fuzzy and rough set methods.
The researchers developed a fuzzy decision support system for diagnosing coronary artery disease, which showed it could estimate the percentage of coronary artery blockage more accurately than cardiologists and angiography.
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.