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Genetic algorithm based on support vector machines for computer vision syndrome classification in health personnel


The inclusion in workplaces of video display terminals has brought multiple benefits for the organization of 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 research, a group of eye and vision-related problems associated with prolonged computer use (known as computer vision syndrome) are studied. The aim is to select the characteristics of the subject that are most relevant for the occurrence of this syndrome, and then, to develop a classification model for its prediction. The estimate 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 the training of the support vector machine with different patterns of parameters, 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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  1. 1.

    Spain (2002) 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. Boletín Oficial del Estado, Nº 274 (15-11-2002), pp 40126–40132

  2. 2.

    Spain (1997) Real decreto 488/1997 sobre disposiciones mínimas de seguridad y salud relativas al trabajo con equipos que incluyen pantallas de visualización. Boletín Oficial del Estado, Nº 97 (23-04-1997), pp 12928–12931

  3. 3.

    Instituto Nacional de Seguridad e Higiene en el trabajo (2006) Guía Técnica para la evaluación y prevención de los riesgos relativos a la utilización de equipos con pantallas de visualización [Internet]. Accessed 15 Jan 2018

  4. 4.

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

  5. 5.

    Bergqvist UO, Knave BG (1994) Eye discomfort and work with visual display terminals. Scand J Work Environ Health 20(1):27–33

  6. 6.

    Fenga C, Aragona P, Di Nola C, Spinella R (2014) 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

  7. 7.

    Ünlü C, Güney E, Akçay BÍS, Akçali G, Erdoğan G, Bayramlar H (2012) 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

  8. 8.

    Kroemer KHE (1997) Design of the Computer Workstation. In: Helander M, Landauer TK, Prasad VE (eds) Handbook of human–computer interaction, 2nd edn. Elsevier, Amsterdam, pp 1395–1414

  9. 9.

    American Optometric Association. Computer Vision Syndrome [Internet]. Accessed 16 Jan 2018

  10. 10.

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

  11. 11.

    González-Pérez M, Susi R, Antona B, Barrio A, González E (2014) The Computer-Vision Symptom Scale (CVSS17): development and initial validation. Invest Ophthalmol Vis Sci 55(7):4504–4511

  12. 12.

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

  13. 13.

    Tauste A, Ronda E, Molina MJ, Seguí M (2016) Effect of contact lens use on computer vision syndrome. Ophthalmic Physiol Opt 36(2):112–119

  14. 14.

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

  15. 15.

    Yazici A, Sari ES, Sahin G et al (2015) Change in tear film characteristics in visual display terminal users. Eur J Ophthalmol 25(2):85–89

  16. 16.

    Rosado P, Lequerica-Fernández P, Villallaín L, Peña I, Sanchez-Lasheras F, de Vicente JC (2013) Survival model in oral squamous cell carcinoma based on clinicopathological parameters, molecular markers and support vector machines. Expert Syst Appl 40(12):4770–4776

  17. 17.

    Alvarez-Menéndez L, de Cos Juez FJ, Lasheras FS, Riesgo JAA (2010) Artificial neural networks applied to cancer detection in a breast screening programme. Math Comput Model 52(7–8):983–991

  18. 18.

    Stamile C, Kocevar G, Cotton F, Sappey-Marinier D (2017) A genetic algorithm-based model for longitudinal changes detection in white matter fiber-bundles of patient with multiple sclerosis. Comput Biol Med 84(1):182–188

  19. 19.

    Antón JCÁ, Nieto PJG, de Cos Juez FJ, Lasheras FS, Viejo CB, Gutiérrez NR (2013) Battery state-of-charge estimator using the MARS technique. IEEE Trans Power Electron 28(8):3798–3805

  20. 20.

    De Cos Juez FJ, Lasheras FS, García Nieto PJ, Suárez MAS (2009) 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

  21. 21.

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

  22. 22.

    Osborn J, De Cos Juez FJ, Guzman D et al (2012) Using artificial neural networks for open-loop tomography. Opt Express 20(3):2420–2434

  23. 23.

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

  24. 24.

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

  25. 25.

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

  26. 26.

    de Cos Juez FJ, Nieto PJG, Torres JM, Castro JT (2010) Analysis of lead times of metallic components in the aerospace industry through a supported vector machine model. Math Comput Model 52(7):1177–1184

  27. 27.

    Lasheras FS, Vilán JAV, Nieto PJG, del Coz Díaz JJ (2010) 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

  28. 28.

    Vapnik VN, Vapnik V (1998) Statistical learning theory, vol 1. Wiley, New York

  29. 29.

    Shawe-Taylor J, Cristianini N (2004) Kernel methods for pattern analysis, 1st edn. Cambridge University Press, Cambridge

  30. 30.

    Mercer J (1909) Functions of positive and negative type and their connection with the theory of integral equations. Philos Trans R Soc A 209(441–458):415–446

  31. 31.

    Holland JH (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT press, Cambridge

  32. 32.

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

  33. 33.

    Galán CO, Lasheras FS, de Cos Juez FJ, Sánchez AB (2017) Missing data imputation of questionnaires by means of genetic algorithms with different fitness functions. J Comput Appl Math 311:704–717

  34. 34.

    Robertson MM, Huang YH, Larson N (2016) 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

  35. 35.

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

  36. 36.

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

  37. 37.

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

  38. 38.

    Kojima T, Ibrahim OM, Wakamatsu T et al (2011) 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

  39. 39.

    Ramin C, Devore EE, Wang W, Pierre-Paul J, Wegrzyn LR, Schernhammer ES (2015) Night shift work at specific age ranges and chronic disease risk factors. Occup Environ Med 72(2):100–107

  40. 40.

    Gu F, Han J, Laden F et al (2015) Total and cause-specific mortality of U.S. nurses working rotating night shifts. Am J Prev Med 48(3):241–252

  41. 41.

    Stocker LJ, Macklon NS, Cheong YC, Bewley SJ (2014) Influence of shift work on early reproductive outcomes: a systematic review and meta-analysis. Obstet Gynecol 124(1):99–110

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Correspondence to Ana Suárez Sánchez.

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Artime Ríos, E., Suárez Sánchez, A., Sánchez Lasheras, F. et al. Genetic algorithm based on support vector machines for computer vision syndrome classification in health personnel. Neural Comput & Applic 32, 1239–1248 (2020).

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  • Support vector machines
  • Genetic algorithms
  • Computer vision syndrome
  • Health personnel