title: Classification of postural profiles among mouth-breathing children by learning vector quantization.
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.
  
  
  OBJECTIVE
  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, J48 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.
authors.affiliation: Department of Health Informatics, Federal University of São Paulo (UNIFESP), São Paulo, Brazil.
authors.forename: F
authors.initials: F
authors.lastname: Mancini
authors.forename: F S
authors.initials: FS
authors.lastname: Sousa
authors.forename: A D
authors.initials: AD
authors.lastname: Hummel
authors.forename: A E J
authors.initials: AE
authors.lastname: Falcão
authors.forename: L C
authors.initials: LC
authors.lastname: Yi
authors.forename: C F
authors.initials: CF
authors.lastname: Ortolani
authors.forename: D
authors.initials: D
authors.lastname: Sigulem
authors.forename: I T
authors.initials: IT
authors.lastname: Pisa
mesh_terms: D000367:Age Factors; D000465:Algorithms; D001185:Artificial Intelligence; D002648:Child; D002669:Child Welfare; D002675:Child, Preschool; D020000:Decision Support Systems, Clinical; D005240:Feasibility Studies; D006801:Humans; D007858:Learning; D009058:Mouth Breathing / Q000473:pathology*; D016571:Neural Networks, Computer; D016011:Normal Distribution; D011187:Posture / Q000502:physiology*; D012372:ROC Curve; D012680:Sensitivity and Specificity; D012984:Software