Angiography is the standard method for diagnosing CAD. This method is an invasive method with certain side effects. Therefore, non-invasive methods should be used to diagnose CAD using clinical data. In this section, an ET-SVMRBF (Extra Tree Support Vector Machine Radial Basis Function) method is proposed for CAD diagnosis using clinical data. The Z-Alizadeh Sani CAD dataset located at UCI (University of California, Irvine) was used to validate this model. The main goal of this strategy is to reduce mortality through timely diagnosis of CAD.