Coronary artery disease diagnosis using extra tree-support vector machine: ET-SVMRBF

Abstract

Coronary Artery Disease (CAD) is a type of cardiovascular disease that can lead to cardiac arrest if not diagnosed timely. Angiography is a standard method adopted to diagnose CAD. This method is an invasive method having certain side effects. So there is a need for non-invasive methods to diagnose CAD using clinical data. In this paper, authors have proposed a methodology ET-SVMRBF (Extra Tree SVM-RBF) to diagnose CAD using clinical data. The Z-Alizadeh Sani CAD data set available on University of California (UCI, Irvine), has been used for validating this methodology. The class imbalance problem in this data set has been resolved using Synthetic Minority OverSampling Technique (SMOTE). Relevant features are selected using the Extra Tree feature selection method. Authors have evaluated the performance of different classifiers Extreme Gradient Boosting (XGBoost), K-NN (K-Nearest Neighbour), Support Vector Machine-Linear (SVM-Linear) and Support Vector Machine-Radial Basis Function (SVM-RBF) on the data set. GridSearch optimisation method is used for hyperparameter optimisation. Accuracy of 95.16% is achieved by ET-SVMRBF which is higher than recent existing work in literature.