Methods Inf Med - Classification of postural profiles among mouth-breathing children by learning vector quantization.

Tópicos

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Resumo

CKGROUND: 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, 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.

Resumo Limpo

ckground mouth breath chronic syndrom may bring postur chang find characterist pattern chang occur complex musculoskelet system mouthbreath children challeng learn vector quantiz lvq artifici neural network model can appli purposeobject aim present studi appli lvq determin characterist postur profil shown mouthbreath children order understand abnorm postur among mouth breathersmethod postur train data children mouth breather nose breather postur valid data children mouth breather nose breather use perform lvq classif model compar relat selforgan map backpropag appli multilay perceptron bayesian network naiv bay j decis tree k knearestneighbor classifi classifi accuraci assess mean leaveoneout crossvalid area roc curv auc interrat agreement kappa statisticsresult use lvq model five postur profil mouthbreath children determin lvq show satisfactori result mouthbreath nosebreath classif sensit specif rate respect use train dataset respect use valid datasetconclus five postur profil mouthbreath children suggest lvq incorpor applic softwar classifi sever mouth breather abnorm postur

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