An Evolutionary-Fuzzy Expert System for the Diagnosis of Coronary Artery Disease

Medical diagnosis is a tedious process in which the result of the diagnosis has to be accurate. In this paper, an evolutionary fuzzy expert system is proposed for the diagnosis of the Coronary Artery Disease (CAD) based on Cleveland clinic foundation datasets for heart diseases. The decision tree is used to select the most significant attributes and the output is converted into crisp if-then rules. The crisp sets of rules are transformed into the fuzzy rules and these rules constitute the fuzzy rule base. Genetic Algorithm (GA) is used to tune the fuzzy membership functions and the optimized of membership functions using GA helps to achieve better accuracy. The performance of the proposed system is analyzed using various parameters like classification accuracy, sensitivity and specificity and it is observed that that this system achieves better accuracy than the existing systems.

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