Machine Learning Based Evaluation of Reading and Writing Difficulties

Mamoru Iwabuchi*, Rumi Hirabayashi, Kenryu Nakamura, Nem Khan Dim

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Citations (Scopus)


The possibility of auto evaluation of reading and writing difficulties was investigated using non-parametric machine learning (ML) regression technique for URAWSS (Understanding Reading and Writing Skills of Schoolchildren) [1] test data of 168 children of grade 1-9. The result showed that the ML had better prediction than the ordinary rule-based decision.

Original languageEnglish
Title of host publicationHarnessing the Power of Technology to Improve Lives
EditorsPeter Cudd, Luc de Witte
PublisherIOS Press
Number of pages4
ISBN (Electronic)9781614997979
Publication statusPublished - 2017
Externally publishedYes

Publication series

NameStudies in Health Technology and Informatics
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365


  • dysgraphia
  • dyslexia
  • evaluation
  • machine learning

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management


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