Abstract
Guillain-Barré Syndrome (GBS) is an autoimmune neurological disorder characterized by a fast evolution. Almost a third of patients with this condition presents breathing difficulty and need a mechanical device to assist them. We aim at creating a diagnostic model of the need for mechanical ventilation in GBS. We use for experimentation a real dataset that contains clinical, serological, and nerve conduction tests data. In this dataset, 41 patients out of a total of 122 required mechanical ventilation. JRip, SVM (Support Vector Machines) with linear kernel and C4.5 are used to create the predictive models. We examine whether selecting the relevant variables in the dataset through filter methods makes possible to increase the accuracy of the model. The methods analyzed are: symmetrical uncertainty, chi squared and information gain. An accurate predictive model was obtained after experimentation.
All three authors equally contributed to this paper.
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Hernández-Torruco, J., Canul-Reich, J., Chávez-Bosquez, O. (2017). Predicting the Need of Mechanical Ventilation in Guillain-Barré Patients Using Machine Learning Algorithms with Relevant Features. In: Pichardo-Lagunas, O., Miranda-Jiménez, S. (eds) Advances in Soft Computing. MICAI 2016. Lecture Notes in Computer Science(), vol 10062. Springer, Cham. https://doi.org/10.1007/978-3-319-62428-0_20
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DOI: https://doi.org/10.1007/978-3-319-62428-0_20
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