Authors :
Isaac Punith Kumar; Hemanth Kumar B N
Volume/Issue :
Volume 8 - 2023, Issue 9 - September
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://tinyurl.com/4dd7kewt
DOI :
https://doi.org/10.5281/zenodo.8394751
Abstract :
Dyslexia, a neurodevelopmental disorder
affecting reading and language skills, poses significant
challenges for affected individuals and their educators.
Early identification and intervention are crucial for
better outcomes. This study explores the application of
machine learning techniques for the prediction of
dyslexia, aiming to provide a timely and accurate
diagnosis. Leveraging a diverse dataset of cognitive,
linguistic, and educational features, we employ state-of-
the-art machine learning algorithms to develop
predictive models. Our research focuses on feature
selection and model optimization, aiming to enhance the
accuracy and generalization capabilities of dyslexia
prediction. The results obtained from this study have the
potential to revolutionize dyslexia diagnosis and facilitate
early intervention strategies, ultimately improving the
quality of life for individuals with dyslexia.
Dyslexia, a neurodevelopmental disorder
affecting reading and language skills, poses significant
challenges for affected individuals and their educators.
Early identification and intervention are crucial for
better outcomes. This study explores the application of
machine learning techniques for the prediction of
dyslexia, aiming to provide a timely and accurate
diagnosis. Leveraging a diverse dataset of cognitive,
linguistic, and educational features, we employ state-of-
the-art machine learning algorithms to develop
predictive models. Our research focuses on feature
selection and model optimization, aiming to enhance the
accuracy and generalization capabilities of dyslexia
prediction. The results obtained from this study have the
potential to revolutionize dyslexia diagnosis and facilitate
early intervention strategies, ultimately improving the
quality of life for individuals with dyslexia.
