Fuzzy expert system approach for coronary artery disease screening using clinical parameters
Coronary artery disease (CAD) affects millions of people all over the world including a major portion in India every year. Although much progress has been done in medical science, but the early detection of this disease is still a challenge for prevention. The objective of this paper is to describe developing of a screening expert system that will help to detect CAD at an early stage. Rules were formulated from the doctors and fuzzy expert system approach was taken to cope with uncertainty present in medical domain. This work describes the risk factors responsible for CAD, knowledge acquisition and knowledge representation techniques, method of rule organisation, fuzzification of clinical parameters and defuzzification of fuzzy output to crisp value. The system implementation is done using object oriented analysis and design. The proposed methodology is developed to assist the medical practitioners in predicting the patients risk status of CAD from rules provided by medical experts. The present paper focuses on rule organisation using the concept of modules, meta-rule base, rule address storage in tree representation and rule consistency checking for efficient search of large number of rules in rule base. The developed system leads to 95.85% sensitivity and 83.33% specificity in CAD risk computation.
expert system modules meta-rules fuzzy knowledge base fuzzy inference engine