Predictive Model for Dyslexia from Eye Fixation Events
Jothi Prabha A1, Bhargavi R2, Harish B3
1Jothi Prabha A, Department of Computing Sciences & Engineering, Vellore Institute of Technology, Chennai (Tamil Nadu), India.
2Bhargavi R, Department of Computing Sciences & Engineering, Vellore Institute of Technology, Chennai (Tamil Nadu), India.
3Harish B, Department of Computing Sciences & Engineering, Vellore Institute of Technology, Chennai (Tamil Nadu), India.
Manuscript received on 16 December 2019 | Revised Manuscript received on 23 December 2019 | Manuscript Published on 31 December 2019 | PP: 235-240 | Volume-9 Issue-1S3 December 2019 | Retrieval Number: A10451291S319/19©BEIESP | DOI: 10.35940/ijeat.A1045.1291S319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Dyslexia is a specific learning disorder where the individual often find difficulty in spelling and reading words fluently. Dyslexia is non-curable but with right remedial support, dyslexics can become highly successful in academics and life. Eye movement patterns during reading process can provide an in-depth understanding about reading disorders caused by dyslexia. Eye movements can be captured using eye-tracker, from which the relationship between how eyes move with respect to the words they read can be understood. In this work, a set of binocular fixation and saccade features were extracted from raw eye tracking data based on statistical measures. Machine learning algorithms such as Random Forest Classifier (RF), Support Vector Machine (SVM) for classification and K-Nearest Neighbor (KNN) were analyzed to output classification models for prediction of dyslexia. KNN gave higher levels of accuracy of 95% compared to SVM and RF over a small feature set of features related to fixations and saccades. These eye features can be used as a basis for developing screening means for prediction of dyslexia. Prediction of dyslexia at an early stage can help children to go for remediation which helps them for academic excellence.
Keywords: Dyslexia, Eye Movements, KNN, RF, SVM.
Scope of the Article: Predictive Analysis