title: Towards a predictive model for Guillain-Barré syndrome.
abstract: The severity of Guillain-Barré 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 C4.5 decision tree, SVM (Support Vector Machines) using a Gaussian kernel, and kNN (k Nearest Neighbour) to predict four GBS subtypes. Accuracies were calculated and averaged across 30 10-fold cross-validation (10-FCV) runs. C4.5 obtained 0.9211 (±0.0109), kNN 0.9179 (±0.0041), and SVM 0.9154 (±0.0069). This is an ongoing research project and further experiments are being conducted.
authors.forename: Jose
authors.initials: J
authors.lastname: Hernandez-Torruco
authors.forename: Juana
authors.initials: J
authors.lastname: Canul-Reich
authors.forename: Juan
authors.initials: J
authors.lastname: Frausto-Solis
authors.forename: Juan Jose
authors.initials: JJ
authors.lastname: Mendez-Castillo
mesh_terms: D003663:Decision Trees; D005544:Forecasting / Q000379:methods*; D020275:Guillain-Barre Syndrome / Q000175:diagnosis*; D006801:Humans; D009431:Neural Conduction; D009460:Neurologic Examination; D012698:Serologic Tests; D060388:Support Vector Machine