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.

Abstract-Learn ing Disability (LD) is a classification including several d isorders in wh ich a ch ild has difficulty in learning in a typical manner, usually caused by an unknown factor or factors . LD affects about 15% of children enrolled in schools. The prediction of learning disability is a comp licated task since the identification of LD fro m diverse features or signs is a co mplicated problem. There is no cure for learning disabilit ies and they are life-long. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some t ime. The aim of this paper is to develop a new algorith m for imputing missing values and to determine the significance of the missing value imputation method and dimensionality reduction method in the perfo rmance of fuzzy and neuro fu zzy classifiers with specific emphasis on prediction of learning d isabilities in school age children. In the basic assessment method for prediction of LD, checklists are generally used and the data cases thus collected fully depends on the mood of children and may have also contain redundant as well as missing values. Therefore, in this study, we are proposing a new algorith m, viz. the correlation based new algorith m for imputing the missing values and Principal Co mponent Analysis (PCA) for reducing the irrelevant attributes. After the study, it is found that, the preprocessing methods applied by us improves the quality of data and thereby increases the accuracy of the classifiers. The system is implemented in Math works Software Mat Lab 7.10. The results obtained from this study have illustrated that the developed missing value imputation method is very good contribution in predict ion system and is capable of improving the performance of a classifier.