Cargando…
An Efficient Machine Learning-Based Feature Optimization Model for the Detection of Dyslexia
Dyslexia is among the most common neurological disorders in children. Detection of dyslexia therefore remains an important pursuit for the research works across various domains which is illustrated by the plethora of work presented in diverse scientific articles. The work presented herein attempted...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9288336/ https://www.ncbi.nlm.nih.gov/pubmed/35855801 http://dx.doi.org/10.1155/2022/8491753 |
_version_ | 1784748450484584448 |
---|---|
author | Ahmad, Nazir Rehman, Mohammed Burhanur El Hassan, Hatim Mohammed Ahmad, Iqrar Rashid, Mamoon |
author_facet | Ahmad, Nazir Rehman, Mohammed Burhanur El Hassan, Hatim Mohammed Ahmad, Iqrar Rashid, Mamoon |
author_sort | Ahmad, Nazir |
collection | PubMed |
description | Dyslexia is among the most common neurological disorders in children. Detection of dyslexia therefore remains an important pursuit for the research works across various domains which is illustrated by the plethora of work presented in diverse scientific articles. The work presented herein attempted to utilize the potential of a unified gaming test of subjects (dyslexia/controls) in tandem with principal components derived from data to detect dyslexia. The work aims to build a machine learning model for dyslexia detection using comprehensive gaming test data. We have attempted to explore the potential of various kernel functions of the Support Vector Machine (SVM) on different number of principal components to reduce the computational complexity. A detection accuracy of 92% is obtained from the radial basis function with 5 components, and the highest detection accuracy obtained from the radial basis function with 3 components is 93%. On the contrary, the Artificial Neural Network(ANN) shows an added advantage with minimal number of hyperparameters with 3 components for obtaining an accuracy of 95%. The comparison of the proposed method with some of the existing works shows efficacy of this method for dyslexia detection. |
format | Online Article Text |
id | pubmed-9288336 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-92883362022-07-17 An Efficient Machine Learning-Based Feature Optimization Model for the Detection of Dyslexia Ahmad, Nazir Rehman, Mohammed Burhanur El Hassan, Hatim Mohammed Ahmad, Iqrar Rashid, Mamoon Comput Intell Neurosci Research Article Dyslexia is among the most common neurological disorders in children. Detection of dyslexia therefore remains an important pursuit for the research works across various domains which is illustrated by the plethora of work presented in diverse scientific articles. The work presented herein attempted to utilize the potential of a unified gaming test of subjects (dyslexia/controls) in tandem with principal components derived from data to detect dyslexia. The work aims to build a machine learning model for dyslexia detection using comprehensive gaming test data. We have attempted to explore the potential of various kernel functions of the Support Vector Machine (SVM) on different number of principal components to reduce the computational complexity. A detection accuracy of 92% is obtained from the radial basis function with 5 components, and the highest detection accuracy obtained from the radial basis function with 3 components is 93%. On the contrary, the Artificial Neural Network(ANN) shows an added advantage with minimal number of hyperparameters with 3 components for obtaining an accuracy of 95%. The comparison of the proposed method with some of the existing works shows efficacy of this method for dyslexia detection. Hindawi 2022-07-09 /pmc/articles/PMC9288336/ /pubmed/35855801 http://dx.doi.org/10.1155/2022/8491753 Text en Copyright © 2022 Nazir Ahmad et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Ahmad, Nazir Rehman, Mohammed Burhanur El Hassan, Hatim Mohammed Ahmad, Iqrar Rashid, Mamoon An Efficient Machine Learning-Based Feature Optimization Model for the Detection of Dyslexia |
title | An Efficient Machine Learning-Based Feature Optimization Model for the Detection of Dyslexia |
title_full | An Efficient Machine Learning-Based Feature Optimization Model for the Detection of Dyslexia |
title_fullStr | An Efficient Machine Learning-Based Feature Optimization Model for the Detection of Dyslexia |
title_full_unstemmed | An Efficient Machine Learning-Based Feature Optimization Model for the Detection of Dyslexia |
title_short | An Efficient Machine Learning-Based Feature Optimization Model for the Detection of Dyslexia |
title_sort | efficient machine learning-based feature optimization model for the detection of dyslexia |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9288336/ https://www.ncbi.nlm.nih.gov/pubmed/35855801 http://dx.doi.org/10.1155/2022/8491753 |
work_keys_str_mv | AT ahmadnazir anefficientmachinelearningbasedfeatureoptimizationmodelforthedetectionofdyslexia AT rehmanmohammedburhanur anefficientmachinelearningbasedfeatureoptimizationmodelforthedetectionofdyslexia AT elhassanhatimmohammed anefficientmachinelearningbasedfeatureoptimizationmodelforthedetectionofdyslexia AT ahmadiqrar anefficientmachinelearningbasedfeatureoptimizationmodelforthedetectionofdyslexia AT rashidmamoon anefficientmachinelearningbasedfeatureoptimizationmodelforthedetectionofdyslexia AT ahmadnazir efficientmachinelearningbasedfeatureoptimizationmodelforthedetectionofdyslexia AT rehmanmohammedburhanur efficientmachinelearningbasedfeatureoptimizationmodelforthedetectionofdyslexia AT elhassanhatimmohammed efficientmachinelearningbasedfeatureoptimizationmodelforthedetectionofdyslexia AT ahmadiqrar efficientmachinelearningbasedfeatureoptimizationmodelforthedetectionofdyslexia AT rashidmamoon efficientmachinelearningbasedfeatureoptimizationmodelforthedetectionofdyslexia |