  Heart disease is one of the most common diseases in middle-aged citizens.
Among the vast number of heart diseases, the coronary artery disease (CAD) is
considered as a common cardiovascular disease with a high death rate. The most
popular tool for diagnosing CAD is the use of medical imaging, e.g.,
angiography. However, angiography is known for being costly and also associated
with a number of side effects. Hence, the purpose of this study is to increase
the accuracy of coronary heart disease diagnosis through selecting significant
predictive features in order of their ranking. In this study, we propose an
integrated method using machine learning. The machine learning methods of
random trees (RTs), decision tree of C5.0, support vector machine (SVM),
decision tree of Chi-squared automatic interaction detection (CHAID) are used
in this study. The proposed method shows promising results and the study
confirms that RTs model outperforms other models.
