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Rule extraction for fuzzy expert system to diagnose Coronary artery disease

In many cases, Coronary artery disease (CAD) results in death. CAD occurs when atherosclerosis blocks the bloodstream to the heart muscle in the coronary arteries. In order to diagnose CAD, there is a gold standard called angiography. However, this method is invasive, risky and expensive. Many researches had been successfully conducted to solve the above problem of angiography by using the data mining technique and fuzzy expert system. This research compares the performance of C4.5, CART, and RIPPER as a fuzzy rules generator to be used on the fuzzy expert system. The results show that the combination between algorithm C4.5 and fuzzy expert system achieves the highest accuracy of 81.82%.