Data-Driven Classification of Dyslexia Using Eye-Movement Correlates of Natural Reading

Abstract

Developmental dyslexia is a reading disability estimated to affect between 5 to 10 percent of the population. Current screening methods are limited as they tell very little about the oculomotor processes underlying natural reading. Investigation of eye-movement correlates of reading using machine learning could enhance detection of dyslexia. Here we used eye-tracking data collected during natural reading of 48 young adults (24 dyslexic, 24 control). We established a set of 67 features containing saccade-, glissade-, fixation-related measures and the reading speed. To detect participants with dyslexic reading patterns, we used a linear support vector machine with 10-fold stratified cross-validation repeated 10 times. For feature selection we used a recursive feature elimination method, and we also considered hyperparameter optimization, both with nested and regular cross-validation. The overall best model achieved a 90.1% classification accuracy, while the best nested model achieved a 75.75% accuracy.

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