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Comparative Study of Decision Trees and Rough Sets for the Prediction of Learning Disabilities in School-Age Children

Dr. Julie M. David, Dr. Kannan Balakrishnan

This paper highlights the study of two classification methods, Rough Sets Theory (RST) and Decision Trees (DT), for the prediction of Learning Disabilities (LD) in school-age children, with an emphasis on applications of data mining. Learning disability prediction is a very complicated task. By using these two classification methods we can easily and accurately predict LD in any child. Also, we can determine the best classification method. In this study, rule mining is performed using the algorithms LEM1 in rough sets and J48 in construction of decision trees. From this study, it is concluded that, the performance of decision trees may be considerably poorer in several important aspects compared to that of rough sets theory. It is found that, for selection of attributes, RST is very useful especially in the case of inconsistent data.