loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Maria Angélica de Oliveira Camargo-Brunetto 1 and André R. Gonçalves 2

Affiliations: 1 State University of Londrina, Brazil ; 2 University of Campinas, Brazil

Keyword(s): Decision Support System, Artificial Neural Networks, COPD, Multilayer Perceptron, Radial Basis Function, Least Mean Squares.

Related Ontology Subjects/Areas/Topics: Biomedical Engineering ; Health Information Systems ; Pattern Recognition and Machine Learning

Abstract: Chronic Obstructive Pulmonary Disease (COPD) is characterized by airflow limitation and the spirometry is one of the tests that can be used to detect such disease. However there is a great problem related to the different ways of interpreting the values provided by spirometric devices, regarding different guidelines and reference values. Artificial Neural Networks (ANN) can be used to help with tasks of diagnosis as that. This work presents the modeling and analysis of three ANN models to classify subjects with COPD, based on different sets of variables: a set of observed measures from spirometry and a set of interpreted values according to the guideline proposed by the American Thoracic Society. The results shown that it is possible to achieve a good accuracy in the diagnosis of COPD using ANNs, besides these features set conducted the COPD identification problem to a nearly linearly separable classification problem.

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.139.235.177

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
de Oliveira Camargo-Brunetto, M. and R. Gonçalves, A. (2013). Diagnosing Chronic Obstructive Pulmonary Disease with Artificial Neural Networks using Health Expert Guidelines. In Proceedings of the International Conference on Health Informatics (BIOSTEC 2013) - HEALTHINF; ISBN 978-989-8565-37-2; ISSN 2184-4305, SciTePress, pages 207-214. DOI: 10.5220/0004234102070214

@conference{healthinf13,
author={Maria Angélica {de Oliveira Camargo{-}Brunetto}. and André {R. Gon\c{C}alves}.},
title={Diagnosing Chronic Obstructive Pulmonary Disease with Artificial Neural Networks using Health Expert Guidelines},
booktitle={Proceedings of the International Conference on Health Informatics (BIOSTEC 2013) - HEALTHINF},
year={2013},
pages={207-214},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004234102070214},
isbn={978-989-8565-37-2},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the International Conference on Health Informatics (BIOSTEC 2013) - HEALTHINF
TI - Diagnosing Chronic Obstructive Pulmonary Disease with Artificial Neural Networks using Health Expert Guidelines
SN - 978-989-8565-37-2
IS - 2184-4305
AU - de Oliveira Camargo-Brunetto, M.
AU - R. Gonçalves, A.
PY - 2013
SP - 207
EP - 214
DO - 10.5220/0004234102070214
PB - SciTePress

- Science and Technology Publications, Lda.
RESOURCES

Proceedings

Papers

Authors

Ontology

CONTACTS

Science and Technology Publications, Lda
Avenida de S. Francisco Xavier, Lote 7 Cv. C,
2900-616 Setúbal, Portugal.

Phone: +351 265 520 185 (National fixed network call)
Fax: +351 265 520 186
Email: info@scitepress.org

EXTERNAL LINKS

PRIMORIS

INSTICC

SCITEVENTS

CROSSREF

PROCEEDINGS SUBMITTED FOR INDEXATION BY:

dblp

Ei Compendex

SCOPUS

Semantic Scholar

Google Scholar

Microsoft Academic