Detecting readers with dyslexia using machine learning with eye tracking measures

@article{Rello2015DetectingRW,
  title={Detecting readers with dyslexia using machine learning with eye tracking measures},
  author={L. Rello and Miguel Ballesteros},
  journal={Proceedings of the 12th Web for All Conference},
  year={2015}
}
  • L. Rello, Miguel Ballesteros
  • Published 2015
  • Computer Science
  • Proceedings of the 12th Web for All Conference
  • Worldwide, around 10% of the population has dyslexia, a specific learning disorder. [...] Key Method The model is trained and evaluated in a 10-fold cross experiment with a dataset composed of 1,135 readings of people with and without dyslexia that were recorded with an eye tracker. Our model, based on a Support Vector Machine binary classifier, reaches 80.18% accuracy using the most informative features. To the best of our knowledge, this is the first time that eye tracking measures are used to predict…Expand Abstract
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