Abstract
Breathing difficulty is a complication present in almost a third of Guillain-Barré Syndrome (GBS) patients. To alleviate this condition a mechanical respiratory device is needed. Anticipating this need is crucial for patients’ recovery. This can be achieved by means of machine learning predictive models. We investigated whether clinical, serological, and nerve conduction features separately can predict the need of mechanical ventilation with high accuracy. In this work, three rule based classifiers are applied to create a diagnostic model for this necessity. JRip, OneR and PART algorithms are analyzed using a real dataset. We performed classification experiments using train-test evaluation scheme. Clinical features were found as the best predictors.
Note: All the authors equally contributed to this paper.
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Hernández-Torruco, J., Canul-Reich, J., Román, D.L. (2018). Rule based classifiers for diagnosis of mechanical ventilation in Guillain-Barré Syndrome. In: Omatu, S., Rodríguez, S., Villarrubia, G., Faria, P., Sitek, P., Prieto, J. (eds) Distributed Computing and Artificial Intelligence, 14th International Conference. DCAI 2017. Advances in Intelligent Systems and Computing, vol 620. Springer, Cham. https://doi.org/10.1007/978-3-319-62410-5_22
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DOI: https://doi.org/10.1007/978-3-319-62410-5_22
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