Classification of Postural Profiles among Mouth-breathing Children by Learning Vector Quantization

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dc.contributor.authorMancini, Felipe [UNIFESP]
dc.contributor.authorSousa, Fernando Sequeira [UNIFESP]
dc.contributor.authorHummel, Anderson Diniz [UNIFESP]
dc.contributor.authorFalcão, Alex Esteves Jaccoud [UNIFESP]
dc.contributor.authorYi, Liu Chiao [UNIFESP]
dc.contributor.authorOrtolani, Cristina Lúcia Feijó
dc.contributor.authorSigulem, Daniel [UNIFESP]
dc.contributor.authorPisa, Ivan Torres [UNIFESP]
dc.contributor.institutionUniversidade Federal de São Paulo (UNIFESP)
dc.contributor.institutionFed Inst Educ Sci & Technol São Paulo
dc.contributor.institutionUniv Paulista
dc.date.accessioned2016-01-24T14:05:54Z
dc.date.available2016-01-24T14:05:54Z
dc.date.issued2011-01-01
dc.description.abstractBackground: Mouth breathing is a chronic syndrome that may bring about postural changes. Finding characteristic patterns of changes occurring in the complex musculoskeletal system of mouth-breathing children has been a challenge. Learning vector quantization (LVQ) is an artificial neural network model that can be applied for this purpose.Objectives: the aim of the present study was to apply LVQ to determine the characteristic postural profiles shown by mouth-breathing children, in order to further understand abnormal posture among mouth breathers.Methods: Postural training data on 52 children (30 mouth breathers and 22 nose breathers) and postural validation data on 32 children (22 mouth breathers and 10 nose breathers) were used. the performance of LVQ and other classification models was compared in relation to self-organizing maps, back-propagation applied to multilayer perceptrons, Bayesian networks, naive Bayes, 148 decision trees, k*, and k-nearest-neighbor classifiers. Classifier accuracy was assessed by means of leave-one-out cross-validation, area under ROC curve (AUC), and inter-rater agreement (Kappa statistics).Results: By using the LVQ model, five postural profiles for mouth-breathing children could be determined. LVQ showed satisfactory results for mouth-breathing and nose-breathing classification: sensitivity and specificity rates of 0.90 and 0.95, respectively, when using the training dataset, and 0.95 and 0.90, respectively, when using the validation dataset.Conclusions: the five postural profiles for mouth-breathing children suggested by LVQ were incorporated into application software for classifying the severity of mouth breathers' abnormal posture.en
dc.description.affiliationFed Univ São Paulo UNIFESP, Dept Hlth Informat, São Paulo, Brazil
dc.description.affiliationFed Inst Educ Sci & Technol São Paulo, São Paulo, Brazil
dc.description.affiliationFed Univ São Paulo UNIFESP, Dept Phys Therapy, São Paulo, Brazil
dc.description.affiliationUniv Paulista, Hlth Sci Inst, São Paulo, Brazil
dc.description.affiliationUnifespFed Univ São Paulo UNIFESP, Dept Hlth Informat, São Paulo, Brazil
dc.description.affiliationUnifespFed Univ São Paulo UNIFESP, Dept Phys Therapy, São Paulo, Brazil
dc.description.sourceWeb of Science
dc.description.sponsorshipCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.format.extent349-357
dc.identifierhttps://dx.doi.org/10.3414/ME09-01-0039
dc.identifier.citationMethods of Information in Medicine. Stuttgart: Schattauer Gmbh-verlag Medizin Naturwissenschaften, v. 50, n. 4, p. 349-357, 2011.
dc.identifier.doi10.3414/ME09-01-0039
dc.identifier.issn0026-1270
dc.identifier.urihttps://repositorio.unifesp.br/handle/11600/33235
dc.identifier.wosWOS:000294694200007
dc.language.isoeng
dc.publisherSchattauer Gmbh-verlag Medizin Naturwissenschaften
dc.relation.ispartofMethods of Information in Medicine
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectNeural networks (computer)en
dc.subjectClinical decision support systemsen
dc.subjectPosture and mouth breathingen
dc.titleClassification of Postural Profiles among Mouth-breathing Children by Learning Vector Quantizationen
dc.typeinfo:eu-repo/semantics/article
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