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Genetic Algorithm Based on Support Vector Machines for Computer Vision Syndrome Classification

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International Joint Conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017, Proceeding (SOCO 2017, ICEUTE 2017, CISIS 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 649))

Abstract

The inclusion in workplaces of video display terminals has introduced multiple benefits in the organization of the work. Nevertheless, it also implies a series of risks for the health of the workers, since it can cause ocular and visual disorders, among others.

In this work, a group of eye and vision-related problems associated to prolonged computer use (known as computer vision syndrome) are studied. The aim is to select the characteristics of the subject most relevant for the occurrence of this syndrome, and then, to develop a classification model for its prediction.

The estimation of this problem is made by means of support vector machines for classification. This machine learning technique will be trained with the support of a genetic algorithm. This provides different patterns of parameters to the training of the support vector machine, improving its performance.

The model performance is verified in terms of the area under the ROC curve, which leads to a model with high accuracy in the classification of the syndrome.

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References

  1. Gobierno de España: Ley 41/2002, de 14 de noviembre, básica reguladora de la autonomía del paciente y de derechos y obligaciones en materia de información y documentación clínica. 2002 (2008). http://www.boe.es/boe/dias/2002/11/15/pdfs/A40126-40132.pdf. Accessed 15 Mar 2016

  2. Scheiman, M.: Accommodative and binocular vision disorders associated with video display terminals: diagnosis and management issues. J. Am. Optom. Assoc. 67(9), 531–539 (1996)

    Google Scholar 

  3. Bergqvist, U.O., Knave, B.G.: Eye discomfort and work with visual display terminals. Scand. J. Work Environ. Health 20(1), 27–33 (1994)

    Article  Google Scholar 

  4. Fenga, C., Aragona, P., Di Nola, C., Spinella, R.: Comparison of ocular surface disease index and tear osmolarity as markers of ocular surface dysfunction in video terminal display workers. Am. J. Ophthalmol. 158(1), 41–48 (2014)

    Article  Google Scholar 

  5. Unlü, C., Güney, E., Akçay, B., Akçali, G., Erdogan, G., Bayramlar, H.: Comparison of ocular-surface disease index questionnaire, tearfilm break-up time, and Schirmer tests for the evaluation of the tearfilm in computer users with and without dry-eye symptomatology. Clin. Ophthalmol. 6, 1303–1306 (2012)

    Article  Google Scholar 

  6. American Optometric Association. Computer Vision Syndrome. Accessed 16 Apr 2017

    Google Scholar 

  7. del Mar Seguí, M., Cabrero-García, J., Crespo, A., Verdú, J., Ronda, E.: A reliable and valid questionnaire was developed to measure computer vision syndrome at the workplace. J. Clin. Epidemiol. 68(6), 662–673 (2015)

    Article  Google Scholar 

  8. González-Pérez, M., Susi, R., Antona, B., Barrio, A., González, E.: The computer-vision symptom scale (CVSS17): development and initial validation. Invest. Ophthalmol. Vis. Sci. 55(7), 4504–4511 (2014)

    Article  Google Scholar 

  9. Ranasinghe, P., et al.: Computer vision syndrome among computer office workers in a developing country: an evaluation of prevalence and risk factors. BMC Res. Notes 9(1), 150 (2016)

    Article  Google Scholar 

  10. Tauste, A., Ronda, E., Molina, M.-J., Seguí, M.: Effect of contact lens use on computer vision syndrome. Ophthalmic Physiol. Opt. 36, 112–119 (2016)

    Article  Google Scholar 

  11. Sa, E.C., Ferreira Junior, M., Rocha, L.E.: Risk factors for computer visual syndrome (CVS) among operators of two call centers in São Paulo, Brazil. Work 41(Supplement 1), 3568–3574 (2012)

    Google Scholar 

  12. Yazici, A., et al.: Change in tear film characteristics in visual display terminal users. Eur. J. Ophthalmol. 25(2), 85–89 (2014)

    Article  Google Scholar 

  13. Antón, J.C.Á., Nieto, P.J.G., de Cos Juez, F.J., Lasheras, F.S., Viejo, C.B., Gutiérrez, N.R.: Battery state-of-charge estimator using the MARS technique. IEEE Trans. Power Electron. 28(8), 3798–3805 (2013)

    Article  Google Scholar 

  14. De Cos Juez, F.J., Sánchez Lasheras, F., García Nieto, P.J.: Suárez Suárez, M.A.: A new data mining methodology applied to the modelling of the influence of diet and lifestyle on the value of bone mineral density in post-menopausal women. Int. J. Comput. Math. 86(10–11), 1878–1887 (2009)

    Article  MATH  Google Scholar 

  15. Sánchez-Lasheras, F., de Andrés, J., Lorca, P., de Cos Juez, F.J.: A hybrid device for the solution of sampling bias problems in the forecasting of firms’ bankruptcy. Expert Syst. Appl. 39(8), 7512–7523 (2012)

    Article  Google Scholar 

  16. Osborn, J., et al.: Using artificial neural networks for open-loop tomography. Opt. Express 20(3), 2420–2434 (2012)

    Article  Google Scholar 

  17. Guzmán, D., de Cos Juez, F.J., Myers, R., Guesalaga, A., Sánchez Lasheras, F.: Modeling a MEMS deformable mirror using non-parametric estimation techniques. Opt. Express 18(20), 21356–21369 (2010)

    Article  Google Scholar 

  18. Guzmán, D., de Cos Juez, F.J., Sánchez Lasheras, F., Myers, R., Young, L.: Deformable mirror model for open-loop adaptive optics using multivariate adaptive regression splines. Opt. Express 18(7), 6492–6505 (2010)

    Article  Google Scholar 

  19. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)

    MATH  Google Scholar 

  20. de Cos Juez, F.J., García Nieto, P.J., Martínes Torres, J., Taboada Castro, J.: Analysis of lead times of metallic components in the aerospace industry through a supported vector machine model. Math. Comput. Model. 52(7), 1177–1184 (2010)

    Article  Google Scholar 

  21. Sánchez Lasheras, F., Vilán Vilán, J.A., García Nieto, P.J., del Coz Díaz, J.J.: The use of design of experiments to improve a neural network model in order to predict the thickness of the chromium layer in a hard chromium plating process. Math. Comput. Model. 52(7), 1169–1176 (2010)

    Article  Google Scholar 

  22. Vapnik, V.N., Vapnik, V.: Statistical Learning Theory, vol. 1. Wiley, New York (1998)

    MATH  Google Scholar 

  23. Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. MIT Press, Cambridge (1992)

    Google Scholar 

  24. De Falco, I., Della Cioppa, A., Tarantino, E.: Mutation-based genetic algorithm: performance evaluation. Appl. Soft Comput. 1(4), 285–299 (2002)

    Article  Google Scholar 

  25. Ordóñez Galán, C., Sánchez Lasheras, F., de Cos Juez, F.J., Bernardo Sánchez, A.: Missing data imputation of questionnaires by means of genetic algorithms with different fitness functions. J. Comput. Appl. Math. 311, 704–717 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  26. Robertson, M.M., Huang, Y., Larson, N.: The relationship among computer work, environmental design, and musculoskeletal and visual discomfort: examining the moderating role of supervisory relations and co-worker support. Int. Arch. Occup. Environ. Health 89(1), 7–22 (2016)

    Article  Google Scholar 

  27. Uchino, M., et al.: Prevalence of dry eye disease and its risk factors in visual display terminal users: the Osaka study. Am. J. Ophthalmol. 156(4), 759–766 (2013)

    Article  Google Scholar 

  28. Rosenfield, M.: Computer vision syndrome: a review of ocular causes and potential treatments. Ophthalmic Physiol. Opt. 31(5), 502–515 (2011)

    Article  Google Scholar 

  29. Portello, J.K., Rosenfield, M., Bababekova, Y., Estrada, J.M., Leon, A.: Computer-related visual symptoms in office workers. Ophthalmic Physiol. Opt. 32(5), 375–382 (2012)

    Article  Google Scholar 

  30. Kojima, T., et al.: The impact of contact lens wear and visual display terminal work on ocular surface and tear functions in office workers. Am. J. Ophthalmol. 152(6), 933–940 (2011)

    Article  Google Scholar 

  31. Suárez Sánchez, A., Riesgo Fernández, P., Sánchez Lasheras, F., de Cos Juez, F.J., García Nieto, P.J.: Prediction of work-related accidents according to working conditions using support vector machines. Appl. PPL Math. Comput. 218(7), 3539–3552 (2011)

    Article  MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. Hospital Universitario Central de Asturias, Oviedo, Spain

    Eva María Artime Ríos

  2. Department of Optics, Pharmacology and Anatomy, University of Alicante, Alicante, Spain

    María del Mar Seguí Crespo

  3. Department of Business Administration, University of Oviedo, Oviedo, Spain

    Ana Suarez Sánchez

  4. Prospecting and Exploitation of Mines Department, University of Oviedo, Oviedo, Spain

    Sergio Luis Suárez Gómez

  5. Department of Construction and Manufacturing Engineering, University of Oviedo, Gijón, Spain

    Fernando Sánchez Lasheras

Authors
  1. Eva María Artime Ríos
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  2. María del Mar Seguí Crespo
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  3. Ana Suarez Sánchez
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  4. Sergio Luis Suárez Gómez
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  5. Fernando Sánchez Lasheras
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Corresponding author

Correspondence to Ana Suarez Sánchez .

Editor information

Editors and Affiliations

  1. University of Leon, Leon, Spain

    Hilde Pérez García

  2. University of Leon, Leon, Spain

    Javier Alfonso-Cendón

  3. University of Leon, Leon, Spain

    Lidia Sánchez González

  4. Department of Industrial Engineering, University of A Coruña, Ferrol, Spain

    Héctor Quintián

  5. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Artime Ríos, E.M., Seguí Crespo, M.d.M., Suarez Sánchez, A., Suárez Gómez, S.L., Sánchez Lasheras, F. (2018). Genetic Algorithm Based on Support Vector Machines for Computer Vision Syndrome Classification. In: Pérez García, H., Alfonso-Cendón, J., Sánchez González, L., Quintián, H., Corchado, E. (eds) International Joint Conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017, Proceeding. SOCO ICEUTE CISIS 2017 2017 2017. Advances in Intelligent Systems and Computing, vol 649. Springer, Cham. https://doi.org/10.1007/978-3-319-67180-2_37

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  • DOI: https://doi.org/10.1007/978-3-319-67180-2_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67179-6

  • Online ISBN: 978-3-319-67180-2

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