ISSN(Online): 2320-9801 ISSN (Print): 2320-9798 International Journal of Innovative Research in Computer and Communication Engineering (An ISO 3297: 2007 Certified Organization) Vol. 2, Issue 7, July 2014 Copyright to IJIRCCE www.ijircce.com 4908 Comparative Study of Decision Trees and Rough Sets for the Prediction of Learning Disabilities in School-Age Children Dr. Julie M. David 1 , Dr. Kannan Balakrishnan 2 1 Dept. of Computer Applications, MES College, Marampally, Aluva Cochin- 683 107, India 2 Dept. of Computer Applications, Cochin University of Science & Technology, Cochin - 682 022, India ABSTRACT: 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. KEYWORDS: Decision Tree, Learning Disability, Rough Sets, Rule Mining, Support and Confidence I. INTRODUCTION This paper presents the comparative study for rough sets and decision trees and shows how these ideas may be utilized for data mining. During the late 1970s and early 1980s, J. Ross Quinlan, a researcher in machine learning developed a decision tree algorithms known as ID3 [8]. This work expanded on earlier work on concept learning system. Decision tree method is widely used in data mining and decision support system. Decision tree is fast and easy to use for rule generation and classification problems. It is an excellent tool for decision representations. For prediction of LD, decision trees are probably the most frequently used tools for rule extraction from data whereas the rough sets based methods seems to be their newer alternative. In both cases, the algorithms are simple and easy to interpret by users. There are very little comparative studies are available. The purpose of the present paper is to show the important differences in performance of two data mining methods for the prediction of LD in children. The rough set approach seems to be of fundamental importance to artificial intelligence and especially in the case of machine learning, knowledge acquisition, decision analysis, knowledge discovery from databases, expert systems, inductive reasoning and pattern recognition [2]. II. RELATED WORK Learning disability is a general term that describes specific kinds of learning problems. Learning disabilities are formally defined in many ways in many countries. The most frequent clause used in determining whether a child has a learning disability is the difference between areas of functioning. When a person shows a great disparity between those areas of functioning in which she or he does well and those in which considerable difficulty is experienced, this child is described as having a learning disability [5]. A learning disability can cause a child to have trouble in learning and using certain skills. The skills most often affected are: reading, writing, listening, speaking, reasoning and doing math [5]. Learning disabilities vary from child to child. One child with LD may not have the same kind of learning problems as another child with LD. There is no "cure" for learning disabilities [9]. With the right help, children with LD can and do learn successfully. If a child has unexpected problems in learning to read, write, listen, speak, or do math, then teachers and parents may want to investigate more.