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

  • S.I. : AI and ML applied to Health Sciences (MLHS)
  • Published:
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Abstract

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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Author information

Authors and Affiliations

  1. Central University Hospital of Asturias, Oviedo, Spain

    Eva María Artime Ríos

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

    Ana Suárez Sánchez

  3. Department of Mathematics, University of Oviedo, Oviedo, Spain

    Fernando Sánchez Lasheras

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

    María del Mar Seguí Crespo

Authors
  1. Eva María Artime Ríos
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  2. Ana Suárez Sánchez
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  3. Fernando Sánchez Lasheras
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  4. María del Mar Seguí Crespo
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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). https://doi.org/10.1007/s00521-018-3581-3

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  • DOI: https://doi.org/10.1007/s00521-018-3581-3

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