SELECTION OF AN ARTIFICIAL NEURAL NETWORK MODEL TO DIAGNOSIS MOUTH-BREATHING CHILDREN

Felipe Mancini, Ivan Torres Pisa, Liu Chiao Yi, Shirley Shizue Nagata Pignatari

2008

Abstract

A number of factors can lead to changes in body posture, basically determined by alterations in the natural curvature of the spine. Such changes, in turn, may also result in secondary health problems. Mouth breathing is thought to be one of these problems. Experiments with healthy nasal breathing individuals have showed that when they are forced to breathe through their mouth only the natural shape of their spine curves change. However the characterization of the spine curvature in mouth breathers has not been done yet and the matter lies on the personal experience of the health professional. This study reports on the preliminary findings of a broader research which attempts to characterize the changes in the behaviour of the spine, caused by mouth breathing, by using artificial neural network modelling and data from 52 subjects. Four different models – backprogation, learning vector quantization (LVQ), and self-organizing map (SOM) – were tested for best performances in sensitivity and specificity in diagnosing mouth and nasal breathing children. Competitive-learning-based algorithms – LVQ and SOM – presented the best performance for current data set.

Paper Citation

in Harvard Style

Mancini F., Torres Pisa I., Chiao Yi L. and Shizue Nagata Pignatari S. (2008). SELECTION OF AN ARTIFICIAL NEURAL NETWORK MODEL TO DIAGNOSIS MOUTH-BREATHING CHILDREN . In Proceedings of the First International Conference on Health Informatics - Volume 2: HEALTHINF, (BIOSTEC 2008) ISBN 978-989-8111-16-6, pages 197-200. DOI: 10.5220/0001042401970200

in Bibtex Style

@conference{healthinf08,
author={Felipe Mancini and Ivan Torres Pisa and Liu Chiao Yi and Shirley Shizue Nagata Pignatari},
title={SELECTION OF AN ARTIFICIAL NEURAL NETWORK MODEL TO DIAGNOSIS MOUTH-BREATHING CHILDREN},
booktitle={Proceedings of the First International Conference on Health Informatics - Volume 2: HEALTHINF, (BIOSTEC 2008)},
year={2008},
pages={197-200},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001042401970200},
isbn={978-989-8111-16-6},
}

in EndNote Style

TY - CONF
JO - Proceedings of the First International Conference on Health Informatics - Volume 2: HEALTHINF, (BIOSTEC 2008)
TI - SELECTION OF AN ARTIFICIAL NEURAL NETWORK MODEL TO DIAGNOSIS MOUTH-BREATHING CHILDREN
SN - 978-989-8111-16-6
AU - Mancini F.
AU - Torres Pisa I.
AU - Chiao Yi L.
AU - Shizue Nagata Pignatari S.
PY - 2008
SP - 197
EP - 200
DO - 10.5220/0001042401970200