Comparing performances of logistic regression, classification and regression tree, and neural networks for predicting coronary artery disease

Authors:
Imran Kurt;Mevlut Ture;A. Turhan Kurum
Affiliations:
Eskisehir Osmangazi University, Medical Faculty, Department of Biostatistics, 26480 Eskisehir, Turkey;Trakya University, Medical Faculty, Department of Biostatistics, 22030 Edirne, Turkey;Trakya University, Medical Faculty, Department of Cardiology, 22030 Edirne, Turkey
Venue:
Expert Systems with Applications: An International Journal
Year:
2008

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Abstract

In this study, performances of classification techniques were compared in order to predict the presence of coronary artery disease (CAD). A retrospective analysis was performed in 1245 subjects (865 presence of CAD and 380 absence of CAD). We compared performances of logistic regression (LR), classification and regression tree (CART), multi-layer perceptron (MLP), radial basis function (RBF), and self-organizing feature maps (SOFM). Predictor variables were age, sex, family history of CAD, smoking status, diabetes mellitus, systemic hypertension, hypercholesterolemia, and body mass index (BMI). Performances of classification techniques were compared using ROC curve, Hierarchical Cluster Analysis (HCA), and Multidimensional Scaling (MDS). Areas under the ROC curves are 0.783, 0.753, 0.745, 0.721, and 0.675, respectively for MLP, LR, CART, RBF, and SOFM. MLP was found the best technique to predict presence of CAD in this data set, given its good classificatory performance. MLP, CART, LR, and RBF performed better than SOFM in predicting CAD in according to HCA and MDS.