Artime Ríos, Eva María, Sánchez Lasheras, Fernando, Suárez Sánchez, Ana, Iglesias-Rodríguez, Francisco J., Seguí-Crespo, Mar
Prediction of Computer Vision Syndrome in Health Personnel by Means of Genetic Algorithms and Binary Regression Trees
Artime Ríos EM, Sánchez Lasheras F, Suárez Sánchez A, Iglesias-Rodríguez FJ, Seguí Crespo MM. Prediction of Computer Vision Syndrome in Health Personnel by Means of Genetic Algorithms and Binary Regression Trees. Sensors. 2019; 19(12):2800. doi:10.3390/s19122800
URI: http://hdl.handle.net/10045/94351
DOI: 10.3390/s19122800
ISSN: 1424-8220
Abstract: 
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.
Keywords:Genetic algorithms, Regression tree, Computer vision syndrome, Health personnel, Occupational health
MDPI
info:eu-repo/semantics/article