Kannan, Balakrishnan; Julie, David M(Springer, September 24, 2013)
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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 15 %
of children enrolled in schools. The prediction of LD is a
vital and intricate job. The aim of this paper is to design an
effective and powerful tool, using the two intelligent methods
viz., Artificial Neural Network and Adaptive Neuro-Fuzzy
Inference System, for measuring the percentage of LD that
affected in school-age children. In this study, we are proposing
some soft computing methods in data preprocessing for
improving the accuracy of the tool as well as the classifier.
The data preprocessing is performed through Principal Component
Analysis for attribute reduction and closest fit algorithm
is used for imputing missing values. The main idea in
developing the LD prediction tool is not only to predict the
LD present in children but also to measure its percentage
along with its class like low or minor or major. The system
is implemented in Mathworks Software MatLab 7.10.
The results obtained from this study have illustrated that the
designed prediction system or tool is capable of measuring
the LD effectively
Description:
Soft Comput (2014) 18:1093–1112
DOI 10.1007/s00500-013-1129-0