Prediction of Learning Disabilities in Children: Development of a New Algorithm in Decision Tree

Learning Disability (LD) is a neurological condition that affects a child's brain and impairs his ability to carry out one or many specific tasks. It is a classification including several disorders in which a child has difficulty learning in a typical manner, usually caused by an unknown factor or factors. LD affects about 15% of children enrolled in schools. An affected child can have normal or above average intelligence. A child with a learning disability is often wrongly labelled as being smart but lazy. Learning disability is not indicative of intelligence level. Rather, children with a learning disability have trouble performing specific types of skills or completing tasks if left to figure things out by themselves or if taught in conventional ways. As there is no cure for learning disabilities 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 time. This paper proposes a systematic approach for identification of LD in school-age children using a modified supervised learning algorithm in decision tree, viz. Modified J48 algorithm. Decision trees with J48 are powerful and popular tool used for classification and prediction in data mining, but it is a failure in handling inconsistent data. So, in this paper, we are introducing the Modified J48 algorithm, which imputes the missing values in the pre-processing stage, and then the tree is pruned. The different rules extracted from the tree have used in prediction of learning disabilities.