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Prediction of Computer Vision Syndrome in Health Personnel by Means of Genetic Algorithms and Binary Regression Trees †

One of the major consequences of the digital revolution has been the increase in the use of electronic devices in health services. Despite their remarkable advantages, though, the use of computers and other visual display terminals for a prolonged time may have negative effects on vision, leading to...

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Detalles Bibliográficos
Autores principales: Artime Ríos, Eva María, Sánchez Lasheras, Fernando, Suárez Sánchez, Ana, Iglesias-Rodríguez, Francisco J., Seguí Crespo, María del Mar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6630344/
https://www.ncbi.nlm.nih.gov/pubmed/31234490
http://dx.doi.org/10.3390/s19122800
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author Artime Ríos, Eva María
Sánchez Lasheras, Fernando
Suárez Sánchez, Ana
Iglesias-Rodríguez, Francisco J.
Seguí Crespo, María del Mar
author_facet Artime Ríos, Eva María
Sánchez Lasheras, Fernando
Suárez Sánchez, Ana
Iglesias-Rodríguez, Francisco J.
Seguí Crespo, María del Mar
author_sort Artime Ríos, Eva María
collection PubMed
description One of the major consequences of the digital revolution has been the increase in the use of electronic devices in health services. Despite their remarkable advantages, though, the use of computers and other visual display terminals for a prolonged time may have negative effects on vision, leading to a greater risk of Computer Vision Syndrome (CVS) among their users. In this study, the importance of ocular and visual symptoms related to CVS was evaluated, and the factors associated with CVS were studied, with the help of an algorithm based on regression trees and genetic algorithms. The performance of this proposed model was also tested to check its ability to predict how prone a worker is to suffering from CVS. The findings of the present research confirm a high prevalence of CVS in healthcare workers, and associate CVS with a longer duration of occupation and higher daily computer usage.
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spelling pubmed-66303442019-08-19 Prediction of Computer Vision Syndrome in Health Personnel by Means of Genetic Algorithms and Binary Regression Trees † Artime Ríos, Eva María Sánchez Lasheras, Fernando Suárez Sánchez, Ana Iglesias-Rodríguez, Francisco J. Seguí Crespo, María del Mar Sensors (Basel) Article One of the major consequences of the digital revolution has been the increase in the use of electronic devices in health services. Despite their remarkable advantages, though, the use of computers and other visual display terminals for a prolonged time may have negative effects on vision, leading to a greater risk of Computer Vision Syndrome (CVS) among their users. In this study, the importance of ocular and visual symptoms related to CVS was evaluated, and the factors associated with CVS were studied, with the help of an algorithm based on regression trees and genetic algorithms. The performance of this proposed model was also tested to check its ability to predict how prone a worker is to suffering from CVS. The findings of the present research confirm a high prevalence of CVS in healthcare workers, and associate CVS with a longer duration of occupation and higher daily computer usage. MDPI 2019-06-22 /pmc/articles/PMC6630344/ /pubmed/31234490 http://dx.doi.org/10.3390/s19122800 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Artime Ríos, Eva María
Sánchez Lasheras, Fernando
Suárez Sánchez, Ana
Iglesias-Rodríguez, Francisco J.
Seguí Crespo, María del Mar
Prediction of Computer Vision Syndrome in Health Personnel by Means of Genetic Algorithms and Binary Regression Trees †
title Prediction of Computer Vision Syndrome in Health Personnel by Means of Genetic Algorithms and Binary Regression Trees †
title_full Prediction of Computer Vision Syndrome in Health Personnel by Means of Genetic Algorithms and Binary Regression Trees †
title_fullStr Prediction of Computer Vision Syndrome in Health Personnel by Means of Genetic Algorithms and Binary Regression Trees †
title_full_unstemmed Prediction of Computer Vision Syndrome in Health Personnel by Means of Genetic Algorithms and Binary Regression Trees †
title_short Prediction of Computer Vision Syndrome in Health Personnel by Means of Genetic Algorithms and Binary Regression Trees †
title_sort prediction of computer vision syndrome in health personnel by means of genetic algorithms and binary regression trees †
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6630344/
https://www.ncbi.nlm.nih.gov/pubmed/31234490
http://dx.doi.org/10.3390/s19122800
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