A rule-based expert system to assess coronary artery disease under uncertainty

Abstract

The coronary artery disease (CAD) occurs from the narrowing and damaging of major blood vessels or arteries. It has become the most life-threatening disease in the world, especially in the South Asian region. Its detection and treatment involve expensive medical facilities. The early detection of CAD, which is a major challenge, can minimize the patients’ suffering and expenses. The major challenge for CAD detection is incorporating numerous factors for detailed analysis. The goal of this study is to propose a new Clinical Decision Support System (CDSS) which may assist doctors in analyzing numerous factors more accurately than the existing CDSSs. In this paper, a Rule-Based Expert System (RBES) is proposed which involves five different Belief Rules, and can predict five different stages of CAD. The final output is produced by combining all BRBs and by using the Evidential Reasoning (ER). Performance evaluation is measured by calculating the success rate, error rate, failure rate and false omission rate. The proposed RBES has higher a success rate and false omission rate than other existing CDSSs.