Analysis of support vectors helps to identify borderline patients in classification studies

In this work a new approach to the support vector machine (SVM) method is taken. Not in developing a new algorithm, but rather in analyzing the result of the performed classification tasks. The SVM approach provides efficient and powerful classification algorithms. SM-Classifiers have a few meta parameters to be tuned, are easy to implement, and are trained through optimization of a quadratic cost function, which ensures the uniqueness of the SVM solution. The SVM solution is given through a linear combination of the training samples which are selected by the SVM optimization procedure. This subset of borderline samples close to the decision boundary can be separated into the samples which are misclassified and those samples that are just classified correctly. The potential drawback of the SVMs being restricted to samples from the dataset is at the same time an advantage in medical applications. Here, we applied this approach to a highly selected group of 44 patients with inducible ventricular tachycardia and a group of 51 healthy subjects.