  Recent years have seen growing interest in the diagnosis of Coronary Artery
Disease (CAD) with machine learning methods to reduce the cost and health
implications of conventional diagnosis. This paper introduces a CAD diagnosis
method with a novel feature extraction technique called the Profile-Based
Binary Feature Extraction (PBBFE). In this method, after partitioning numerical
features, frequent itemsets are extracted by the Apriori algorithm and then
used as features to increase the CAD diagnosis accuracy. The proposed method
consists of two main phases. In the first phase, each patient is assigned a
profile based on age, gender, and medical condition, and then all numerical
features are discretized based on assigned profiles. All features then undergo
a binarization process to become ready for feature extraction by Apriori. In
the last step of this phase, frequent itemsets are extracted from the dataset
by Apriori and used to build a new dataset. In the second phase, the Genetic
Algorithm and the Support Vector Machine are used to identify the best subset
of extracted features for classification. The proposed method was tested on the
Z-Alizadeh Sani dataset, which is one the richest databases in the field of
CAD. Performance comparisons conducted on this dataset showed that the proposed
method outperforms all major alternative methods with 98.35% accuracy, 100%
sensitivity, and 94.25% specificity. The proposed method also achieved the
highest accuracy on several other datasets.
