Toward a machine learning model for a primary diagnosis of Guillain-Barré syndrome subtypes

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MetadadosDescriçãoIdioma
Autor(es): dc.creatorAlarcón-Narváez, Daniel-
Autor(es): dc.creatorHernández-Torruco, José-
Autor(es): dc.creatorHernández-Ocaña, Betania-
Autor(es): dc.creatorChávez-Bosquez, Oscar-
Autor(es): dc.creatorMarchi, Jerusa-
Autor(es): dc.creatorMéndez-Castillo, Juan José-
Data de aceite: dc.date.accessioned2026-02-09T11:10:38Z-
Data de disponibilização: dc.date.available2026-02-09T11:10:38Z-
Data de envio: dc.date.issued2022-02-14-
Data de envio: dc.date.issued2022-02-14-
Data de envio: dc.date.issued2020-
Fonte completa do material: dc.identifierhttps://repositorio.ufla.br/handle/1/49329-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/1135336-
Descrição: dc.descriptionGuillain-Barré Syndrome (GBS) is a neurological disorder affecting people of any age and sex, mainly damaging the peripheral nervous system. GBS is divided into several subtypes, in which only four are the most common, demanding different treatments. Identifying the subtype is an expensive and time-consuming task. Early GBS detection is crucial to save the patient’s life and not aggravate the disease. This work aims to provide a primary screening tool for GBS subtypes fast and efficiently without complementary invasive methods, based only on clinical variables prospected in consultation, taken from clinical history, and based on risk factors. We conducted experiments with four classifiers with different approaches, five different filters for feature selection, six wrappers, and One versus All (OvA) classification. For the experiments, we used a data set that includes 129 records of Mexican patients and 26 clinical representative variables. Random Forest filter obtained the best results in each classifier for the diagnosis of the four subtypes, in the same way, this filter with the SVM classifier achieved the best result (0.6840). OvA with SVM classifier reached a balanced accuracy of 0.8884 for the Miller-Fisher (MF) subtype.-
Formato: dc.formatapplication/pdf-
Idioma: dc.languageen-
Publicador: dc.publisherSAGE Journals-
Direitos: dc.rightsAttribution-NonCommercial 4.0 International-
Direitos: dc.rightsAttribution-NonCommercial 4.0 International-
Direitos: dc.rightsacesso aberto-
Direitos: dc.rightshttp://creativecommons.org/licenses/by-nc/4.0/-
Direitos: dc.rightshttp://creativecommons.org/licenses/by-nc/4.0/-
???dc.source???: dc.sourceHealth Informatics Journal-
Palavras-chave: dc.subjectFeature selection methods-
Palavras-chave: dc.subjectMulticlass classification-
Palavras-chave: dc.subjectSingle classifiers-
Palavras-chave: dc.subjectPerformance measures-
Palavras-chave: dc.subjectPredictive model-
Título: dc.titleToward a machine learning model for a primary diagnosis of Guillain-Barré syndrome subtypes-
Tipo de arquivo: dc.typeArtigo-
Aparece nas coleções:Repositório Institucional da Universidade Federal de Lavras (RIUFLA)

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