Significance of Classification Techniques in Prediction of Learning Disabilities

TL;DR

This paper demonstrates the effectiveness of decision tree and clustering techniques, specifically J48 and K-means algorithms, in predicting learning disabilities among school-age children, aiding early diagnosis.

Contribution

It introduces the application of decision tree and clustering methods for identifying learning disabilities, highlighting their importance in educational and clinical assessments.

Findings

Decision trees can effectively predict learning disabilities.

Clustering reveals distinct signs and symptoms of LD.

The combined approach improves early detection of LD.

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.