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Learning disability prediction tool using ANN and ANFIS

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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.

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Authors and Affiliations

  1. Department of Computer Applications, MES College Marampally, Aluva, Cochin, 683 107, India

    Julie M. David

  2. Department of Computer Applications, Cochin University of Science and Technology, Cochin, 682 022, India

    Kannan Balakrishnan

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  1. Julie M. David
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  2. Kannan Balakrishnan
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Correspondence to Julie M. David.

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Communicated by G. Acampora.

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David, J.M., Balakrishnan, K. Learning disability prediction tool using ANN and ANFIS. Soft Comput 18, 1093–1112 (2014). https://doi.org/10.1007/s00500-013-1129-0

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  • DOI: https://doi.org/10.1007/s00500-013-1129-0

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