Publication Date: 2023/09/30
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.
Keywords: No Keywords Available
DOI: https://doi.org/10.5281/zenodo.8394751
PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT23SEP389.pdf
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