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