A comparison of non-symmetric entropy-based classification trees and support vector machine for cardiovascular risk stratification

• Published in: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society Vol. 2011; pp. 79 - 82
• Main Authors: Singh, Anima, Guttag, John V.
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01.01.2011
• Subjects: Acute Coronary Syndrome - mortality; Algorithm design and analysis; Diagnosis, Computer-Assisted - methods; Entropy; History; Humans; Kernel; Machine learning; Pattern Recognition, Automated - methods; Prevalence; Proportional Hazards Models; Reproducibility of Results; Risk Assessment - methods; Risk Factors; Sensitivity and Specificity; Support Vector Machine; Support vector machines; Survival Analysis; Survival Rate; Training
• ISBN: 9781424441211; 1424441218
• ISSN: 1094-687X; 1557-170X
• DOI: 10.1109/IEMBS.2011.6089901

Classification tree-based risk stratification models generate easily interpretable classification rules. This feature makes classification tree-based models appealing for use in a clinical setting, provided that they have comparable accuracy to other methods. In this paper, we present and evaluate the performance of a non-symmetric entropy-based classification tree algorithm. The algorithm is designed to accommodate class imbalance found in many medical datasets. We evaluate the performance of this algorithm, and compare it to that of SVM-based classifiers, when applied to 4219 non-ST elevation acute coronary syndrome patients. We generated SVM-based classifiers using three different strategies for handling class imbalance: cost-sensitive SVM learning, synthetic minority oversampling (SMOTE), and random majority undersampling. We used both linear and radial basis kernel-based SVMs. Our classification tree models outperformed SVM-based classifiers generated using each of the three techniques. On average, the classification tree models yielded a 14% improvement in G-score and a 21% improvement in F-score relative to the linear SVM classifiers with the best performance. Similarly, our classification tree models yielded a 12% improvement in G-score and a 21% improvement in the F-score over the best RBF kernel-based SVM classifiers.