Volume 1, Issue 1, 2021
Articles

A Study on different Classification Models for predicting Dyslexia

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  • Vani Chakraborty
Vani Chakraborty
Department of Computer Science[PG], Kristu Jayanti College
DOI: https://doi.org/10.59176/kjcs.v1i1.1264

Published 2020-11-03

Keywords

  • Dyslexia, Eye tracking, detection , Machine Learning, Classification models.

How to Cite

Chakraborty, V. (2020). A Study on different Classification Models for predicting Dyslexia. Kristu Jayanti Journal of Computational Sciences (KJCS), 1(1), 29–36. https://doi.org/10.59176/kjcs.v1i1.1264
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Abstract

Eye tracking technology is used to record the eye positions and the movements of the eye using the optical tracking of corneal reflections. Eye tracking data collected this way can be used in a wide variety of applications like gaming, marketing, cognitive ability and psychology. One of the applications of eye tracking data is to predict whether an individual has a learning disability like Dyslexia. Dyslexia is the most common neurological learning disability which manifests in the form of difficulty in reading and spelling. Although eye tracking data is recorded and available, there is a scarcity of studies done in analysing the data and understanding the hidden relationship and classifying it appropriately. This research intends to study different classification models like Logistic Regression, Gaussian NB, SVC, Decision Tree and so on. that are applied in the prediction of risk of Dyslexia. The paper also presents the result of the accuracy of different classification models in predicting the risk of Dyslexia.

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