This paper highlights the prediction of Learning
Disabilities (LD) in school-age children using two classification
methods, Support Vector Machine (SVM) and Decision Tree (DT),
with an emphasis on applications of data mining. About 10% of
children enrolled in school have a learning disability. Learning
disability prediction in school age children is a very complicated
task because it tends to be identified in elementary school where
there is no one sign to be identified. By using any of the two
classification methods, SVM and DT, we can easily and accurately
predict LD in any child. Also, we can determine the merits and
demerits of these two classifiers and the best one can be selected for
the use in the relevant field. In this study, Sequential Minimal
Optimization (SMO) algorithm is used in performing SVM and J48
algorithm is used in constructing decision trees.
Description:
(IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 2 (2) , 2011, 829-835
Kannan, Balakrishnan; Julie, David M(November 2, 2010)
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Abstract:
The aim of this study is to show the importance of two classification techniques, viz. decision tree and
clustering, in prediction of learning disabilities (LD) of school-age children. LDs affect about 10 percent of
all children enrolled in schools. The problems of children with specific learning disabilities have been a
cause of concern to parents and teachers for some time. Decision trees and clustering are powerful and
popular tools used for classification and prediction in Data mining. Different rules extracted from the
decision tree are used for prediction of learning disabilities. Clustering is the assignment of a set of
observations into subsets, called clusters, which are useful in finding the different signs and symptoms
(attributes) present in the LD affected child. In this paper, J48 algorithm is used for constructing the
decision tree and K-means algorithm is used for creating the clusters. By applying these classification
techniques, LD in any child can be identified