Kannan, Balakrishnan; David Julie, M(Springer, May 15, 2011)
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Abstract:
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. LD affects about 10%
of children enrolled in schools. There is no cure for learning
disabilities and they are lifelong. The problems of children
with specific learning disabilities have been a cause of
concern to parents and teachers for some time. Just as there
are many different types of LDs, there are a variety of tests
that may be done to pinpoint the problem The information
gained from an evaluation is crucial for finding out how the
parents and the school authorities can provide the best
possible learning environment for child. This paper proposes
a new approach in artificial neural network (ANN) for
identifying LD in children at early stages so as to solve the
problems faced by them and to get the benefits to the students,
their parents and school authorities. In this study, we
propose a closest fit algorithm data preprocessing with
ANN classification to handle missing attribute values. This
algorithm imputes the missing values in the preprocessing
stage. Ignoring of missing attribute values is a common
trend in all classifying algorithms. But, in this paper, we use
an algorithm in a systematic approach for classification,
which gives a satisfactory result in the prediction of LD. It
acts as a tool for predicting the LD accurately, and good
information of the child is made available to the concerned
Description:
Neural Comput & Applic (2012) 21:1757–1763
DOI 10.1007/s00521-011-0619-1