Framework of Dyslexia Detection Using Machine Learning Algorithms

A neurologically based learning disability, dyslexia affects 12% of the global population, particularly young children (7–13 years old). The degree of difficulty a child has with reading, spelling, and speaking varies from person to person with dyslexia due to genetic differences in brain development. It effects can fluctuate depending on the types of dyslexia. The lack of intelligence or willingness to study is not the cause. Dyslexia is not curable, but early diagnosis can help individuals with dyslexia to manage their symptoms. Recent studies have identified several genes that may incline an individual to developing dyslexia. Individuals with dyslexia need early identification and intervention to succeed academically and realize their full potential. To help identify dyslexia, we developed a predictive machine learning model and an online gamified exam. Our findings demonstrate that dyslexia can be detected using a computational approach. More people have already utilized an online screening tool based on our methods. The objective of our project is to build a framework which consider few assessment measures to analyze their level of dyslexia. These tests make it easy to recognize dyslexic people quickly.