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

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.