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 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.
Note: The first three authors equally contributed to this paper.
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Hernández-Torruco, J., Canul-Reich, J., Frausto-Solis, J., Méndez-Castillo, J.J. (2015). A Kernel-Based Predictive Model for Guillain-Barré Syndrome. In: Pichardo Lagunas, O., Herrera Alcántara, O., Arroyo Figueroa, G. (eds) Advances in Artificial Intelligence and Its Applications. MICAI 2015. Lecture Notes in Computer Science(), vol 9414. Springer, Cham. https://doi.org/10.1007/978-3-319-27101-9_20
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DOI: https://doi.org/10.1007/978-3-319-27101-9_20
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