A Study of dyslexia using different machine learning algorithm with data mining techniques

  • Abstract
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  • Abstract

    There are many children’s were affected by dyslexia problem over the world. This paper is focusing on medical diagnostic problem – detecting and diagnosing children who were affected by dyslexia based on checklist containing the symptoms and signs of dyslexia using Artificial Neural Network techniques applied with WEKA. Many researchers research identifying or diagnosing dyslexia or non-dyslexia children in many ways. But the drawback in the existing system is they are research based on either Intellectual Intelligent (IQ) or Emotion-al Intelligent (EQ). They are not given accurate result for detecting the dyslexia children. The person’s achievement in life is depends upon both knowledgeable and emotional intelligence. IQ is not only giving successful in life. We must need EQ also. The aim of the present research is to propose a quicker and more efficient technique of diagnosing the problem, leading to timely treatment of the children.

  • References

      [1] Disability World, 2003,”UNICEF and Disabled Children and Youths”, Disability World, No. 19, (online at www.disabil ityworld.org/ 06 − 08 03/index.htm).

      [2] Athanasios S. Drigas and Rodi-Eleni Ioannidou,”A Review on Artificial Intelligence in Special Education”, Ag.Paraskevi, 15310, Athens, Greece, 2013.

      [3] Julie M. David, Kannan Balakrishnan: Machine Learning Approach for Prediction of Learning Disabilities in School Age Children, Int. J. of Computer Applications, ISSN-0975-8887, 9(10), Nov. 2010, pp 7-14. http://www.ijcaonline.org/archives/volume9/ number 1/1432-1931.

      [4] Manghirmalani et al, “Learning Disability Diagnosis and Classification-a Soft Computing Approach”, IEEE World Congress on Information and Communication Technologies (WICT); https://doi.org/10.1109/WICT.2011.6141292.

      [5] Kohli, M., Prasad, T.V, “Identifying Dyslexic Students by Using Artificial Neural Networks”, Proceedings of the World Congress on Engineering, London, U.K, vol. 1(2010).

      [6] Anuradha, J et al, “Diagnosis of ADHD using SVM algorithm”, Proceedings of the Third Annual ACM Bangalore Conference (2010) https://doi.org/10.1145/1754288.1754317.

      [7] Hernadez, J et al, “Learning Difficulties Diagnosis for Children’s Basic Education using Expert Systems”, WSEAS Transactions on Information Science and Applications (2009).

      [8] Jain et al, “Computational Diagnosis of Learning Disability”, International Journal of Recent Trends in Engineering (2009).

      [9] Arthi. K and Tamilarasi, A, ”Prediction of autistic disorder using neuro fuzzy system by applying ANN technique”, International Journal of Developmental Neuroscience 26, 699–704 (2008).

      [10] Lineu C. Fonseca et al, “Quantitative EEG in children with learning disabilities”, Analysis of band power, 64(2-B):376-381, 2006.

      [11] Martin Macas et al, “Bioinspired methods for analysis and classification of reading eye movements of dyslexic children”, Department of Cybernetics, Czech Technical University in Prague, Czech Republic NiSls Symposium 2005.

      [12] Md. Mustafizur Rahman, S. M. Ferdous, Syed Ishtiaque, “Increasing Intelligibility in the Speech of the Autistic Children by an Interactive Computer Game”, Multimedia(ISM), pp 383 – 387, 2010.

      [13] Joan F. Santos, NiritBrosh, Tiago H. Falk, Lonnie Zwaigenbaum, Susan E. Bryson, Wendy Roberts, Isabel M. Smith, Peter Szatmari and Jessica A. Brian, “Very early detection of autism spectrum disorders based on acoustic analysis of pre-verbal vocalizations of 18-month old toddlers”, International Conference on Acoustics, Speech and Signal Processing, pp 7567 – 7571, 2013.

      [14] Suresh P. and Raja, B. K, 2011, “A Review on Analysis and Quantification of Specific Learning Disability (SLD) with fMRI using Image Processing Techniques”, IJCA Proceedings on International Conference on VLSI, Communications and Instrumentation (ICVCI) , vol. 5, pp. 24-29, Foundation of Computer Science.

      [15] Prud’hommeaux et al., “Classification of atypical language inautism”, in Proceedings of the 2nd Workshop on Cognitive Modelingand Computational Linguistics, pp: 88-96, 2011.

      [16] Kathleen T Quach et al., “Application of Artificial Neural Networksin Classification of Autism Diagnosis Based on Gene ExpressionSignatures”.

      [17] Alexander Genkin et al., “Large-scale Bayesian logistic regression fortext categorization”, Technometrics, pp: 291-304, 2007. RachnaAhuja et al, / (IJCSIT) International Journal of Computer Science and Information Technologies https://doi.org/10.1198/004017007000000245.

      [18] Morris, R. D., 1988, “Classification of learning disabilities: Old problems and new approaches”, Journal of Consulting and Clinical Psychology, vol. 56, no. 6, pp.789-794. https://doi.org/10.1037/0022-006X.56.6.789.

      [19] Folorunsho, Olaiya. "Comparative Study of Different Data Mining Techniques Performance in knowledge Discovery from Medical Database." International Journal 3, no. 3 (2013).

      [20] Cohen, I.L., Sudhalter, V., Landong-Jimenez, D. and Keogh, M., 1993, “A Neural Network Approach to the Classification of Autism”, Journal of Autism and Developmental Disorders, vol. 23, no. 3, pp. 443-466. https://doi.org/10.1007/BF01046050.

      [21] James Freeman and David Skapura, “Neural networks: Algorithms, applications and Programming Techniques”, Pearson Education, 2007.

      [22] Michael W.Berry et.al,” Lecture notes in data mining”, World Scientific(2006).

      [23] Eibe Frank, Mark A. Hall, and Ian H. Witten (2016). The WEKA Workbench. Online Appendix for "Data Mining: Practical Machine Learning Tools and Techniques", Morgan Kaufmann, Fourth Edition, 2016.

      [24] C. J. C. Burges, “A Tutorial on Support Vector Machines for Pattern Recognition,” Data Mining and Knowledge Discovery, Vol. 2, 1998, pp. 121-167. https://doi.org/10.1023/A:1009715923555.

      [25] Fabian, H. P., Chan, K. S., Ho, K. Y., & Leong, S. K. (2004). A Study on Decision Tree. Second Engineering & Technology Student’s Congress. Kota Kinabalu. SKTM.

      [26] Chaurasia, V. and Pal, S. (2013) Data Mining Approach to Detect Heart Disease. International Journal of Advanced Computer Science and Information Technology (IJACSIT), 2, 56-66.

      [27] UM, Ashwinkumar, and Anandakumar KR. "Predicting Early Detection of Cardiac and Diabetes Symptoms using Data Mining Techniques.", IEEE,pp 161-165,201 Folorunsho, Olaiya. "Comparative Study of Different Data Mining Techniques Performance in knowledge Discovery from Medical Database." International Journal 3, no. 3 (2013).

      [28] Iyer, A., Jeyalatha, S. and Sumbaly, R. (2015) Diagnosis of Diabetes Using Classification Mining Techniques. International Journal of Data Mining & Knowledge Management Process (IJDKP), 5, 1-14. https://doi.org/10.5121/ijdkp.2015.5101.

      [29] Huang, Feixiang; Wang, Shengyong; Chan, Chien-Chung, "Predicting disease by using data mining based on healthcare information system," Granular Computing (GrC), 2012 IEEE International Conference on , vol., no., pp.191,194, 11-13 Aug. 2012.




Article ID: 21691
DOI: 10.14419/ijet.v7i4.21691

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