Identifying Chinese Children with Dyslexia Using Machine Learning with Character Dictation

Purpose: Dyslexia is characterized by its diverse causes and heterogeneous manifestations. Chinese children with dyslexia exhibit orthographic, phonological, and semantic deficits across character and radical levels when writing. However, whether character dictation can be used to distinguish children with dyslexia from their typically developing peers remains unexplored. Method: A dataset of written characters from 1,015 Chinese children with and without dyslexia from Grades 2-6 was used to train multiple machine models with different learning algorithms. Results: The multi-level multidimensional model reached a predictive accuracy of 78.0%, with stroke, grade, lexicality, and character configuration manifesting as the most predictive features. The accuracy of the model improved to 80.0% when only these features were included. Conclusion: These results not only provide evidence for the multidimensional causes of Chinese dyslexia, but also highlight the utility of machine learning in distinguishing children with dyslexia from their peers via Chinese dictation, which elucidates a promising area of future research.