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

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

Author Mancini, F. Autor UNIFESP Google Scholar
Sousa, F. S. Autor UNIFESP Google Scholar
Hummel, A. D. Autor UNIFESP Google Scholar
Falcao, A. E. J. Autor UNIFESP Google Scholar
Yi, L. C. Autor UNIFESP Google Scholar
Ortolani, C. F. Google Scholar
Sigulem, D. Autor UNIFESP Google Scholar
Pisa, I. T. Autor UNIFESP Google Scholar
Institution Universidade Federal de São Paulo (UNIFESP)
Fed Inst Educ Sci & Technol São Paulo
Univ Paulista
Abstract Background: 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.
Keywords Neural networks (computer)
clinical decision support systems
posture and mouth breathing
Language English
Sponsor Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
Date 2011-01-01
Published in Methods of Information in Medicine. Stuttgart: Schattauer Gmbh-verlag Medizin Naturwissenschaften, v. 50, n. 4, p. 349-357, 2011.
ISSN 0026-1270 (Sherpa/Romeo, impact factor)
Publisher Schattauer Gmbh-verlag Medizin Naturwissenschaften
Extent 349-357
Access rights Open access Open Access
Type Article
Web of Science ID WOS:000294694200007

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