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Article:Machine Learning Approach for Prediction of Learning Disabilities in School-Age Children

by Julie M. David, Kannan Balakrishnan
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 9 - Number 11
Year of Publication: 2010
Authors: Julie M. David, Kannan Balakrishnan
10.5120/1432-1931

Julie M. David, Kannan Balakrishnan . Article:Machine Learning Approach for Prediction of Learning Disabilities in School-Age Children. International Journal of Computer Applications. 9, 11 ( November 2010), 7-14. DOI=10.5120/1432-1931

@article{ 10.5120/1432-1931,
author = { Julie M. David, Kannan Balakrishnan },
title = { Article:Machine Learning Approach for Prediction of Learning Disabilities in School-Age Children },
journal = { International Journal of Computer Applications },
issue_date = { November 2010 },
volume = { 9 },
number = { 11 },
month = { November },
year = { 2010 },
issn = { 0975-8887 },
pages = { 7-14 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume9/number11/1432-1931/ },
doi = { 10.5120/1432-1931 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:58:18.111425+05:30
%A Julie M. David
%A Kannan Balakrishnan
%T Article:Machine Learning Approach for Prediction of Learning Disabilities in School-Age Children
%J International Journal of Computer Applications
%@ 0975-8887
%V 9
%N 11
%P 7-14
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper highlights the two machine learning approaches, viz. Rough Sets and Decision Trees (DT), for the prediction of Learning Disabilities (LD) in school-age children, with an emphasis on applications of data mining. Learning disability prediction is a very complicated task. By using these two approaches, we can easily and accurately predict LD in any child and also we can determine the best classification method. In this study, in rough sets the attribute reduction and classification are performed using Johnson’s reduction algorithm and Naive Bayes algorithm respectively for rule mining and in construction of decision trees, J48 algorithm is used. From this study, it is concluded that, the performance of decision trees are considerably poorer in several important aspects compared to rough sets. It is found that, for selection of attributes, rough sets is very useful especially in the case of inconsistent data and it also gives the information about the attribute correlation which is very important in the case of learning disability.

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Index Terms

Computer Science
Information Sciences

Keywords

Decision Tree Learning Disability Rough Sets Rule Mining Support Confidence

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