A comparative study of kernel-based vector machines with probabilistic outputs for medical diagnosis

Abstract

In this paper, support vector machines (SVMs), least squares SVMs (LSSVMs), relevance vector machines (RVMs), and probabilistic classification vector machines (PCVMs), are compared on sixteen binary and multiclass medical datasets. Particular emphasis is put on the comparison among the commonly used Gaussian radial basis function (GRBF) kernel, and the relatively new generalized min–max (GMM) kernel and exponentiated-GMM (eGMM) kernel. Since most medical decisions involve uncertainty, a postprocessing approach based on Platt’s method and pairwise coupling is employed to produce probabilistic outputs for prediction uncertainty assessment. The extensive empirical study illustrates that the SVM classifier using the tuning-free GMM kernel (SVM-GMM) shows good usability and broad applicability, and exhibits competitive performance against some state-of-the-art methods. These results indicate that SVM-GMM can be used as the first-choice method when selecting an appropriate kernel-based vector machine for medical diagnosis. As an illustration, SVM-GMM efficiently achieves a high accuracy of 98.92% on the thyroid disease dataset consisting of 7200 samples.