Support Vector Machines and logistic regression to predict temporal artery biopsy outcomes

Objective: Support vector machines (SVM) is a newer statistical method that has been reported to be advantageous to traditional logistic regression for clinical classification. We determine if SVM can better predict the results of temporal artery biopsy (TABx) for giant cell arteritis compared to logistic regression. Method: A database of 530 TABx patients with 10 covariates was used and randomly split into training and test sets. The area under the receiving operating curve (AUC), misclassification rate (MCR), and false negative rate (FN) were compared for SVM and logistic regression. AUC and MCR were used to tune the SVM. Results: The SVM model with optimal AUC had gamma = 0.01267 and cost = 26.466, with 133 support vectors. The AUC/MCR/FN for logistic regression and SVM respectively were 0.827/0.184/0.524 and 0.825/0.168/0.571. Conclusion: In our dataset of 530 TABx subjects, SVM did not offer any distinct advantage over the logistic regression prediction model.