Abstract
Guillain-Barré Syndrome (GBS) is an autoimmune neurological disorder characterized by a fast evolution. Complications of this disorder vary among the different subtypes. In this study, we use a real dataset that contains clinical, serological, and nerve conduction test data obtained from 129 GBS patients. We apply three different decision tree classifiers: C4.5, C5.0 and random forest to predict GBS subtypes in two classification scenarios: four subtype classification and One vs All (OVA) classification. We evaluate performance under train-test scenario. Experimental results showed comparable performance among all classifiers, although C5.0 slightly outperformed both C4.5 and random forest. Further experiments are being conducted. This is an ongoing research project.
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Canul-Reich, J., Frausto-Solis, J., Hernández-Torruco, J., Méndez-Castillo, J.J. (2016). Combination of Trees for Guillain-Barré Subtype Classification. In: Omatu, S., et al. Distributed Computing and Artificial Intelligence, 13th International Conference. Advances in Intelligent Systems and Computing, vol 474. Springer, Cham. https://doi.org/10.1007/978-3-319-40162-1_8
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DOI: https://doi.org/10.1007/978-3-319-40162-1_8
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