Using Machine Learning Techniques for Predicting and Diagnosing of Dyslexia in School Age Children

H. Selvi and M.S. Saravanan

As per survey of world health organization, today there are many children’s were affected by dyslexia overall the globe. Identifying and diagnosing students with dyslexia is a complex task because of the ambiguity in selecting the best procedure which required a lot of manpower and resources and due to the lack of nationally agreed standard. In this research, identifying students with dyslexia is based on checklist containing the symptoms and signs of dyslexia using ANN techniques with data mining concepts. An early identification of warning signs, proper assessment, and effective intervention can help dyslexia children to succeed in school life. The main aim of this present research is to detect and diagnosis the dyslexia children based on both IQ and EQ. We expected that they give accurate result for identifying dyslexia students using ANN techniques with data mining concept. So this will helps in early identification of dyslexia in children and reduces the diagnosis time.

Volume 11 | 04-Special Issue

Pages: 753-760