In the last few decades, statistical methods and machine learning (ML) algorithms have become efficient in medical decision-making. Coronary artery disease (CAD) is a common type of cardiovascular disease that causes many deaths each year. In this study, two CAD datasets from different countries (TRNC and Iran) are tested to understand the classification efficiency of different supervised machine learning algorithms. The Z-Alizadeh Sani dataset contained 303 individuals (216 patient, 87 control), while the Near East University (NEU) Hospital dataset contained 475 individuals (305 patients, 170 control). This study was conducted in three stages: (1) Each dataset, as well as their merged version, was subject to review separately with a random sampling method to obtain train-test subsets. (2) The NEU Hospital dataset was assigned as the training data, while the Z-Alizadeh Sani dataset was the test data. (3) The Z-Alizadeh Sani dataset was assigned as the training data, while the NEU hospital dataset was the test data. Among all ML algorithms, the Random Forest showed successful results for its classification performance at each stage. The least successful ML method was kNN which underperformed at all pitches. Other methods, including logistic regression, have varying classification performances at every step.

In this p a p e r , the new method of broadbanwhich utilizes the parametric representation of B m e functions is applied t o double mtchmg problems. Exmples are presented t o exhibit the use of the "paramtric-broadband mtchg" procedure. The results obtained i n this work are ccmpared with tho= obtained via real frequency-direct canpltational technique.It is concluded that the real frquency-direct canpltational and the pararretric methods of broadband mtchmg yield similar results with the same canplmity of the mtchg n e t w k . On the other hand, i f the canplmity of the equalizer is increased, the " p r a~t r i c " approach offers high nunerical stability with less canputation.