An Efficient Machine Learning Model for Prediction of Dyslexia from Eye Fixation Events
Dyslexia is a learning condition in which a person has difficulty spelling and reading words correctly. Dyslexia is not curable, although dyslexics can achieve great success in school and in life with the right remedial support. Eye movement patterns during reading can help you understand dyslexia-related reading issues better. An eye-tracker can be used to capture eye movements and deduce the relationship between how eyes move in response to the words they read. In this study, raw eye tracking data was used to construct a collection of binocular fixation and saccade features based on statistical measurements. In this study, raw eye tracking data was used to construct a collection of binocular fixation and saccade features based on statistical measurements. To develop classification models for dyslexia prediction, machine learning techniques such as the Random Forest Classifier (RF), the Support Vector Machine (SVM) for classification, and the K-Nearest Neighbor (KNN) for prediction of dyslexia were examined. KNN exhibited 95 percent accuracy over a limited feature set related with fixations and saccades, compared to SVM and RF. These properties of the eyes can be used to create dyslexia prediction screening tools. Early discovery of dyslexia can help children get the treatment they need, helping them to succeed in school.
Author (S) Details
A. Jothi Prabha
Jyothishmathi Institute of Technology & Science, India.
Vellore Institute of Technology University, Chennai, India.
VIT University, Chennai Campus, India.
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