  How to accurately classify and diagnose whether an individual has Coronary
Stenosis (CS) without invasive physical examination? This problem has not been
solved satisfactorily. To this end, the four machine learning (ML) algorithms,
i.e., Boosted Tree (BT), Decision Tree (DT), Logistic Regression (LR) and
Random Forest (RF) are employed in this paper. First, eleven features including
basic information of an individual, symptoms and results of routine physical
examination are selected, as well as one label is specified, indicating whether
an individual suffers from different severity of coronary artery stenosis or
not. On the basis of it, a sample set is constructed. Second, each of these
four ML algorithms learns from the sample set to obtain the corresponding
optimal classified results, respectively. The experimental results show that:
RF performs better than other three algorithms, and the former algorithm
classifies whether an individual has CS with an accuracy of 95.7% (=90/94).
