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Recent years have seen growing interest in the diagnosis of Coronary ArteryDisease (CAD) with machine learning methods to reduce the cost and healthimplications of conventional diagnosis. This paper introduces a CAD diagnosismethod with a novel feature extraction technique called the Profile-BasedBinary Feature Extraction (PBBFE). In this method, after partitioning numericalfeatures, frequent itemsets are extracted by the Apriori algorithm and thenused as features to increase the CAD diagnosis accuracy. The proposed methodconsists of two main phases. In the first phase, each patient is assigned aprofile based on age, gender, and medical condition, and then all numericalfeatures are discretized based on assigned profiles. All features then undergoa binarization process to become ready for feature extraction by Apriori. Inthe last step of this phase, frequent itemsets are extracted from the datasetby Apriori and used to build a new dataset. In the second phase, the GeneticAlgorithm and the Support Vector Machine are used to identify the best subsetof extracted features for classification. The proposed method was tested on theZ-Alizadeh Sani dataset, which is one the richest databases in the field ofCAD. Performance comparisons conducted on this dataset showed that the proposedmethod outperforms all major alternative methods with 98.35% accuracy, 100%sensitivity, and 94.25% specificity. The proposed method also achieved thehighest accuracy on several other datasets.

A Profile-Based Binary Feature Extraction Method Using Frequent Itemsets for Improving Coronary Artery Disease Diagnosis