Screening Dyslexia Using Visual Auditory Computer Games and Machine Learning

Abstract

Reading acquisition is one the main keys for school success and a crucial component for empowering individuals to participate meaningfully in society. Yet, it is still a challenging skill to acquire for around 10% of children that have dyslexia, a type of neuro-developmental disorder that affects the ability to learn how to read and write. Dyslexia is often under-diagnosed, and normally children with dyslexia are only detected once they fail in school, even though dyslexia is not related to general intelligence. In this work, we present an approach for screening dyslexia using language-independent games in combination with machine learning models. To reach this goal, we designed the content of a computer game, collected data from 137 children playing this game (51 with dyslexia) in different languages -German, Spanish and English- and created a prediction model using different machine learning classifiers. Our method provides a precision of 0.78 and recall of 0.79 for German and a precision of 0.83 and recall of 0.80 for all languages when Extra Trees are used, with an accuracy of 0.67 and 0.75, respectively. Our results open the possibility of inexpensive online early screening of dyslexia for young children using non-linguistic elements.