Classification of postural profiles among mouth-breathing children by learning vector quantization
| Autor: | Daniel Sigulem, Fernando Sequeira Sousa, Ivan Torres Pisa, Alex Esteves Jaccoud Falcão, Anderson Diniz Hummel, L. C. Yi, Felipe Mancini, Cristina Lucia Feijó Ortolani |
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| Rok vydání: | 2009 |
| Předmět: |
020205 medical informatics
Speech recognition Posture Decision tree Normal Distribution Child Welfare Health Informatics Mouth breathing 02 engineering and technology Sensitivity and Specificity 03 medical and health sciences Naive Bayes classifier 0302 clinical medicine C4.5 algorithm Cohen's kappa Health Information Management Artificial Intelligence 0202 electrical engineering electronic engineering information engineering medicine Humans Learning 030212 general & internal medicine Child Mathematics Advanced and Specialized Nursing Learning vector quantization Age Factors Bayesian network Mouth Breathing Perceptron Decision Support Systems Clinical ROC Curve Child Preschool Feasibility Studies Neural Networks Computer medicine.symptom Algorithms Software |
| Zdroj: | Methods of information in medicine. 50(4) |
| ISSN: | 2511-705X |
| Popis: | SummaryBackground: 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. |
| Databáze: | OpenAIRE |
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