Abstract—This paper highlights the prediction of learning disabilities (LD) in school-age children using rough set theory (RST) with an emphasis on application of data mining. In rough sets, data analysis start from a data table called an information system, which contains data about objects of interest, characterized in terms of attributes. These attributes consist of the properties of learning disabilities. By finding the relationship between these attributes, the redundant attributes can be eliminated and core attributes determined. Also, rule mining is performed in rough sets using the algorithm LEM1. The prediction of LD is accurately done by using Rosetta, the rough set tool kit for analysis of data. The result obtained from this study is compared with the output of a similar study conducted by us using Support Vector Machine (SVM) with Sequential Minimal Optimisation (SMO) algorithm. It is found that, using the concepts of reduct and global covering, we can easily predict the learning disabilities in children.

Index Terms—Global Covering, Indiscernibility Relation, Learning Disability, Reduct and Core

Julie M. David is with MES College, Aluva, Cochin – 683 107, India, as Asst. Professor in the Department of Computer Applications. (phone: +91-9447104152/9447434303; fax : +91- 484- 2678587; e-mail : julieeldhosem@yahoo.com)
Kannan Balakrishnan is with Cochin University of Science & Technology,Cochin – 682 022, India, as Reader in the Department of Computer Applications. (e-mail:mullayilkannan@gmail.com)

Cite: Julie M. David and Kannan Balakrishnan, "Prediction of Key Symptoms of Learning Disabilities in School-Age Children Using Rough Sets," International Journal of Computer and Electrical Engineering vol. 3, no. 1, pp. 163-169, 2011.