Skip to main content
SpringerLink
Log in

Prediction of Dyslexia Using Machine Learning—A Research Travelogue

  • Conference paper
  • First Online:
Proceedings of the Third International Conference on Microelectronics, Computing and Communication Systems

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 556))

Abstract

Dyslexia is regarded as a common learning disorder characterized by a persistent deficit in rapid word recognition and by spelling. It affects the individual’s ability to decode letters and words fluently and accurately. The research community has worked on distinguishing dyslexic from non-dyslexic people by using various machine learning approaches, image processing techniques, design assessment, and assistive tools to support dyslexia. This survey paper looks at different dimensions of research toward dyslexia. This review identifies the research gaps, open issues, and challenges in this field. It also motivates for the application of ML techniques toward early prediction of dyslexia.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Frid A, Breznitz Z An SVM based algorithm for analysis and discrimination of dyslexic readers from regular readers using ERPs. In: 2012, IEEE 27th convention of electrical & electronics engineers in Israel, pp 1–4

    Google Scholar 

  2. Perera H, Fairuz Md, Wong KW (2016) A review of electroencephalogram-based analysis and classification frameworks for dyslexia. In: International conference on neural information processing. Springer International Publishing

    Google Scholar 

  3. Iwabuchi M, Hirabayashi R, Nakamura K, Dim NK (2017) Machine learning based evaluation of reading and writing difficulties. Stud Health Technol Info 242:1001

    Google Scholar 

  4. Rello L, Ballesteros Detecting readers with dyslexia using machine learning with eye tracking measures. In: Proceedings of the 12th web for all conference. ACM, p 16

    Google Scholar 

  5. Palacios A, Sánchez C, Destercke An extension of the FURIA classification algorithm to low quality data through fuzzy rankings and its application to the early diagnosis of dyslexia. Neuro Comput 176:60–71

    Google Scholar 

  6. Al-Barhamtoshy HM, Motaweh DM (2017) Diagnosis of dyslexia using computation analysis. In: Informatics, health & technology (ICIHT), pp 1–7. IEEE

    Google Scholar 

  7. Kohli M, Prasad TV (2010) Identifying dyslexic students by using artificial neural networks. In: Proceedings of the world congress on engineering, vol 1. London, UK

    Google Scholar 

  8. Zainuddin L, Mansor, Mahmoodin Optimized KNN classify rule for EEG based differentiation between capable dyslexic and normal children. In: Biomedical engineering and sciences (IECBES), 2016 IEEE, pp 685–688

    Google Scholar 

  9. Jain M, Dongardive, Abraham Computational diagnosis of learning disability. Int J Recent Trends Eng 2(3)

    Google Scholar 

  10. Płoński G, Altarelli M, Marbach VE, Grande, Jednoróg (2017) Multi‐parameter machine learning approach to the neuroanatomical basis of developmental dyslexia. Human Brain Map 38(2):900–908

    Google Scholar 

  11. El-Baz A et al (2008) A new CAD system for early diagnosis of dyslexic brains. In: 15th IEEE international conference on image processing, 2008. ICIP 2008. IEEE

    Google Scholar 

  12. Tamboer V, Ghebreab, Scholte (2016) Machine learning and dyslexia: classification of individual structural neuro-imaging scans of students with and without dyslexia. NeuroImage Clin 11:508–514

    Google Scholar 

  13. Feng Z, Yang T, Xie, Ding (2017) Dyslexic children show atypical cerebellar activation and cerebro-cerebellar functional connectivity in orthographic and phonological processing. Cerebellum 16(2):496–507

    Google Scholar 

  14. Cui X, Gong G (2016) Disrupted white matter connectivity underlying developmental dyslexia: a machine learning approach. Hum Brain Mapp 37(4):1443–1458

    Article  Google Scholar 

  15. Morken H, Hugdahl, Specht (2017) Reading in dyslexia across literacy development: a longitudinal study of effective connectivity. NeuroImage 144:92–100

    Google Scholar 

  16. Mohamad, Mansor, Lee Review of neurological techniques of diagnosing dyslexia in children. In: 2013, 3rd international conference on system engineering and technology (ICSET), pp 389–393. IEEE

    Google Scholar 

  17. Martins, Lima, Sampaio M Mobile application to support dyslexia diagnostic and reading practice

    Google Scholar 

  18. Alsobhi, Yaquob A, Khan N, Rahanu H (2015) Personalised learning materials based on dyslexia types: ontological approach. Proc Comput Sci 60:113–121

    Google Scholar 

  19. Wang R, Chen L, Solheim I, Schulz T, Ayesh A (2017) Conceptual motivation modeling for students with dyslexia for enhanced assistive learning. In: Proceedings of the 2017 ACM workshop on “intelligent interfaces for ubiquitous and smart learning”, pp 11–18. ACM

    Google Scholar 

  20. Sarpudin SNS, Zambri S (2014) Web readability for students with dyslexia: Malaysian case study. In: 3rd international conference on user science and engineering (i-USEr). IEEE, pp 192–197

    Google Scholar 

  21. Manghirmalani PZ, Jain K (2011) Learning disability diagnosis and classification—a soft computing approach. In: 2011 world congress on information and communication technologies (WICT). IEEE, pp 479–484

    Google Scholar 

  22. Chen A et al (2017) Individualized early prediction of familial risk of dyslexia: a study of infant vocabulary development. Front Psychol 8

    Google Scholar 

  23. Loizou, Laouris (2011) Developing prognosis tools to identify learning difficulties in children using machine learning technologies. Cogn Comput 3(3):490–500

    Google Scholar 

  24. Georgopoulos, Stylios (2009) Diagnosis support using fuzzy cognitive maps combined with genetic algorithms. In: Engineering in medicine and biology society EMBC 2009. “Annual international conference of the IEEE”, pp 6226–6229. IEEE

    Google Scholar 

  25. Mahmoodin Z et al Selection of symlets wavelet function order for EEG signal feature extraction in children with dyslexia. In: Biomedical engineering & sciences (ISSBES) in student symposium in IEEE

    Google Scholar 

  26. Dimitriadis, Stavros (2016) Classifying children with reading difficulties from non-impaired readers via symbolic dynamics and complexity analysis of MEG resting-state data. In: Signal processing and information technology (ISSPIT), international symposium on IEEE

    Google Scholar 

  27. Atakan VH et al (2009) Early prediction of reading disability using machine learning. In: Annual symposium proceedings, vol 2009. American medical informatics association

    Google Scholar 

  28. Schmalz X, Altoè, G Mulatti C (2016) Statistical learning and dyslexia: a systematic review. Annals Dyslexia, 1–16

    Google Scholar 

  29. Mahmoodin et al (2016) Electroencephalogram electrode localization in the support vector machine classification of dyslexic children. In: EMBS conference on biomedical engineering and sciences (IECBES). IEEE

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computing Science and Engineering, Vellore Institute of Technology, Chennai, India

    A. Jothi Prabha & R. Bhargavi

Authors
  1. A. Jothi Prabha
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. R. Bhargavi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. Jothi Prabha .

Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, Birla Institute of Technology, Mesra, Ranchi, Jharkhand, India

    Vijay Nath

  2. Department of Computer Science and Engineering, University of Kalyani, Kalyani, West Bengal, India

    Jyotsna Kumar Mandal

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Jothi Prabha, A., Bhargavi, R. (2019). Prediction of Dyslexia Using Machine Learning—A Research Travelogue. In: Nath, V., Mandal, J. (eds) Proceedings of the Third International Conference on Microelectronics, Computing and Communication Systems. Lecture Notes in Electrical Engineering, vol 556. Springer, Singapore. https://doi.org/10.1007/978-981-13-7091-5_3

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-7091-5_3

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-7090-8

  • Online ISBN: 978-981-13-7091-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation