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dc.contributor.authorGuzmán-de-los-Riscos, Eduardo Francisco 
dc.contributor.authorBelmonte-Martínez, María Victoria 
dc.contributor.authorLélis Carvalho, Viviane Marie
dc.date.accessioned2022-06-07T12:27:17Z
dc.date.available2022-06-07T12:27:17Z
dc.date.issued2022-03-18
dc.identifier.citationGuzmán, E., Belmonte, M.-V., & Lelis, V. M. (2022). Ensemble methods for meningitis aetiology diagnosis. Expert Systems, e12996. https://doi.org/10.1111/exsy.12996es_ES
dc.identifier.urihttps://hdl.handle.net/10630/24310
dc.description.abstractIn this work, we explore data-driven techniques for the fast and early diagnosis concerning the etiological origin of meningitis, more specifically with regard to differentiating between viral and bacterial meningitis. We study how machine learning can be used to predict meningitis aetiology once a patient has been diagnosed with this disease. We have a dataset of 26,228 patients described by 19 attributes, mainly about the patient's observable symptoms and the early results of the cerebrospinal fluid analysis. Using this dataset, we have explored several techniques of dataset sampling, feature selection and classification models based both on ensemble methods and on simple techniques (mainly, decision trees). Experiments with 27 classification models (19 of them involving ensemble methods) have been conducted for this paper. Our main finding is that the combination of ensemble methods with decision trees leads to the best meningitis aetiology classifiers. The best performance indicator values (precision, recall and f-measure of 89% and an AUC value of 95%) have been achieved by the synergy between bagging and NBTrees. Nonetheless, our results also suggest that the combination of ensemble methods with certain decision tree clearly improves the performance of diagnosis in comparison with those obtained with only the corresponding decision tree.es_ES
dc.description.sponsorshipThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. We would like to thank the Health Department of the Brazilian Government for providing the dataset and for authorizing its use in this study. We would also like to express our gratitude to the reviewers for their thoughtful comments and efforts towards improving our manuscript. Funding for open access charge: Universidad de Málaga / CBUA.es_ES
dc.language.isoenges_ES
dc.publisherWileyes_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectMeningitises_ES
dc.subject.otherClassificationes_ES
dc.subject.otherEnsemble methodses_ES
dc.subject.otherMachine learninges_ES
dc.subject.otherMeningitis diagnosises_ES
dc.titleEnsemble methods for meningitis aetiology diagnosises_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.centroE.T.S.I. Informáticaes_ES
dc.identifier.doihttps://doi.org/10.1111/exsy.12996
dc.rights.ccAttribution-NonCommercial-NoDerivatives 4.0 Internacional*


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