Dyslexia Prediction Using Machine Learning Algorithms – A Review

Document Type : Primary Research paper


1 Research Scholar, Bishop Heber College (Autonomous), Tiruchirappalli- 620017

2 Assistant professor, Department of Computer Science, Bishop Heber College (Autonomous), Tiruchirappalli-620017,Affiliated to Bharathidasan University.


Dyslexia is a specific Learning disability that can cause difficulties in Reading, Writing and Spelling. It disturbs the parts of the brain that process linguistic This disease is passed in family lines through genes(hereditary) or through new genetic mutations. There are 6 different types of dyslexia’s primary dyslexia, Secondary or Developmental dyslexia, Trauma dyslexia, Visual Dyslexia, Auditory dyslexia and Dysgraphia. Various machine learning algorithms to detect dyslexia they are Random Forest, Decision Tree, Support Vector Machine (SVM), Neural Networks and Bayesian classifiers. Many parameters are used to identify dyslexia eye tracking, fixation and saccadic eye movements and front face detected. This survey paper view at various dimensions of research toward dyslexia. This review finds the research holes, challenges and opportunities in this field. It also encourages to use Machine Learning (ML) algorithms in this research area.