A Kernel-Based Predictive Model for Guillain-Barré Syndrome

Abstract

The severity of Guillain-Barre Syndrome (GBS) varies among subtypes, which can be mainly Acute Inflammatory Demyelinating Polyneuropathy (AIDP), Acute Motor Axonal Neuropathy (AMAN), Acute Motor Sensory Axonal Neuropathy (AMSAN) and Miller-Fisher Syndrome (MF). In this study, we use a real dataset that contains clinical, serological, and nerve conduction tests data obtained from 129 GBS patients. We apply Support Vector Machines (SVM) using four different kernels: linear, Gaussian, polynomial and Laplacian to predict four GBS subtypes. We compare SVM results with those of C4.5. We evaluated performance under both 10-FCV and train-test scenarios. Experimental results showed performance of both classifiers are comparable. SVM slightly outperformed C4.5 with Polynomial kernel in 10-FCV. And it did with Laplacian, polynomial and Gaussian kernels in train-test. This is an ongoing research project and further experiments are being conducted.