ARTIFICIAL NEURAL NETWORK APPROACH FOR OBESITY-HYPERTENSION CLASSIFICATION

Octavian Postolache, Joaquim Mendes, Gabriela Postolache, Pedro Silva Girão

Abstract

One of the newest targets of public health is management of obesity-hypertension. In this paper is presented the use of an artificial neural network based model for objective classification of obesity-hypertension. Different neural network architectures as part of hybrid processing scheme including comparators and competitive processing blocks were developed and tested. The neural network functionality is the classification of the individuals according to the obesity risks. The results show that the neural network classifier is consistent with the standard criteria suggested by the obesity and hypertension guidelines.

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Paper Citation


in Harvard Style

Postolache O., Mendes J., Postolache G. and Silva Girão P. (2009). ARTIFICIAL NEURAL NETWORK APPROACH FOR OBESITY-HYPERTENSION CLASSIFICATION . In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2009) ISBN 978-989-8111-65-4, pages 514-520. DOI: 10.5220/0001553705140520


in Bibtex Style

@conference{biosignals09,
author={Octavian Postolache and Joaquim Mendes and Gabriela Postolache and Pedro Silva Girão},
title={ARTIFICIAL NEURAL NETWORK APPROACH FOR OBESITY-HYPERTENSION CLASSIFICATION},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2009)},
year={2009},
pages={514-520},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001553705140520},
isbn={978-989-8111-65-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2009)
TI - ARTIFICIAL NEURAL NETWORK APPROACH FOR OBESITY-HYPERTENSION CLASSIFICATION
SN - 978-989-8111-65-4
AU - Postolache O.
AU - Mendes J.
AU - Postolache G.
AU - Silva Girão P.
PY - 2009
SP - 514
EP - 520
DO - 10.5220/0001553705140520