Detection of Dyslexia and Dyscalculia in Children

Learning disabilities represent a prevalent category of developmental challenges seen inchildren. The process of learning is pivotal for a child's holistic growth. Children encounterhurdles in their daily tasks such as reading, verbal expression, organizational skills, andsimilar activities. These specific learning impediments are categorized into dyslexia,dysgraphia, and dyscalculia. Dyslexia refers to difficulties in reading and distinguishingspeech sounds. Dysgraphia and dyscalculia pertain to challenges in written expression andmathematical computations, respectively. Timely identification and assessment are crucialfor early intervention and treatment. The forthcoming article outlines methodologies andapproaches employed in identifying dyslexia. Its key contribution lies in a comparativeexamination of diverse machine learning algorithms utilized for dyslexia diagnosis,encompassing SVM, KNN, and random forest.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-04-02
DOI
https://doi.org/10.5281/zenodo.15123066
Primary Topic
Freshwater macroinvertebrate diversity and ecology
Type
article
Field-Weighted Citation Impact
0.00
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article

Detection of Dyslexia and Dyscalculia in Children

Zenodo (CERN European Organization for Nuclear Research)
Freshwater macroinvertebrate diversity and ecology
article

Detection of Dyslexia and Dyscalculia in Children

article en

Abstract

Learning disabilities represent a prevalent category of developmental challenges seen inchildren. The process of learning is pivotal for a child's holistic growth. Children encounterhurdles in their daily tasks such as reading, verbal expression, organizational skills, andsimilar activities. These specific learning impediments are categorized into dyslexia,dysgraphia, and dyscalculia. Dyslexia refers to difficulties in reading and distinguishingspeech sounds. Dysgraphia and dyscalculia pertain to challenges in written expression andmathematical computations, respectively. Timely identification and assessment are crucialfor early intervention and treatment. The forthcoming article outlines methodologies andapproaches employed in identifying dyslexia. Its key contribution lies in a comparativeexamination of diverse machine learning algorithms utilized for dyslexia diagnosis,encompassing SVM, KNN, and random forest.

Zenodo (CERN European Organization for Nuclear Research)
Quality Education
Openalex Percentile: Top 51%
Freshwater macroinvertebrate diversity and ecology
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