Feature Selection Based Fuzzy Expert System for Efficient Diagnosis of Coronary Artery Disease

Coronary artery disease (CAD) is one of the most dangerous diseases which lead to sudden cardiac death. According to Word Health Organization, CAD is the number one killer in the developed world, with over 7.4 million deaths attributed it. Before, CAD is not common disease in Nigeria, however, is at this moment gaining much popularity in the country following the rising number of health issues related to CAD diseases, including higher death rate, which is mostly due to lack of proper awareness among the common people. The diagnosis of CAD is very expensive and time consuming which made computer scientists to use artificial techniques such as expert system to diagnose CAD’s patients. Therefore, in this work, fuzzy based expert system for efficient diagnosis of coronary artery disease has been developed, implemented and evaluated. Hence, the system has archived 90.08% overall accuracy which is very excellent, thus the accuracy determines the proportion of the total number of predictions that were correct. At the same time, the system has 91.30% accuracy to classify of normal patients correctly by the system (specificity) and 90.24% accuracy to classify abnormal patients correctly by the system (sensitivity). This showed that, the system performed efficiently and excellently to diagnose CAD.