{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T16:51:20Z","timestamp":1774371080678,"version":"3.50.1"},"reference-count":33,"publisher":"Wiley","license":[{"start":{"date-parts":[[2018,11,5]],"date-time":"2018-11-05T00:00:00Z","timestamp":1541376000000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100010785","name":"Universidad Ju\u00e1rez Aut\u00f3noma de Tabasco","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100010785","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003141","name":"Consejo Nacional de Ciencia y Tecnolog\u00eda","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003141","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2018,11,5]]},"abstract":"<jats:p>Nowadays, Machine Learning methods have proven to be highly effective on the identification of various types of diseases, in the form of predictive models. Guillain\u2013Barr\u00e9 syndrome (GBS) is a potentially fatal autoimmune neurological disorder that has barely been studied with computational techniques and few predictive models have been proposed. In a previous study, single classifiers were successfully used to build a predictive model. We believe that a predictive model is imperative to carry out adequate treatment in patients promptly. We designed three classification experiments: (1) using all four GBS subtypes, (2) One versus All (OVA), and (3) One versus One (OVO). These experiments use a real-world dataset with 129 instances and 16 relevant features. Besides, we compare five state-of-the-art ensemble methods against 15 single classifiers with 30 independent runs. Standard performance measures were used to obtain the best classifier in each experiment. Derived from the experiments, we conclude that Random Forest showed the best results in four GBS subtypes classification, no ensemble method stood out over the rest in OVA classification, and single classifiers outperformed ensemble methods in most cases in OVO classification. This study presents a novel predictive model for classification of four subtypes of Guillain\u2013Barr\u00e9 syndrome. Our model identifies the best method for each classification case. We expect that our model could assist specialized physicians as a support tool and also could serve as a basis to improved models in the future.<\/jats:p>","DOI":"10.1155\/2018\/1576927","type":"journal-article","created":{"date-parts":[[2018,11,5]],"date-time":"2018-11-05T23:30:30Z","timestamp":1541460630000},"page":"1-10","source":"Crossref","is-referenced-by-count":3,"title":["A Predictive Model for Guillain\u2013Barr\u00e9 Syndrome Based on Ensemble Methods"],"prefix":"10.1155","volume":"2018","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1893-1332","authenticated-orcid":true,"given":"Juana","family":"Canul-Reich","sequence":"first","affiliation":[{"name":"Divisi\u00f3n Acad\u00e9mica de Inform\u00e1tica y Sistemas, Universidad Ju\u00e1rez Aut\u00f3noma de Tabasco, Tabasco, 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