Diagnostic And Classification System For Kids With Learning Disabilities - Rehman, Ullah Khan; Lee, Julia Ai Cheng; Yin, Bee Oon (2017)
Most experts are using manual techniques to diagnose dyslexia. Machine learning algorithms are capable enough to learn the knowledge of experts and thus, automation of the diagnosis process is possible. In this research, we propose an automated diagnostic and classification system. The system is trained by pre-classified data of 857 school children scores in spelling and reading. The twenty-fifth percentile was applied on the scores to label the data. The scores of the twenty-fifth percentile and below were marked as indicators of children who were likely to have dyslexia while the scores above the twenty-fifth percentile were considered to be indicators of children who were non-dyslexic. The system has three components: the diagnostic module is a pre-screening application that can be used by experts, trained users and parents for detecting the symptoms of dyslexia. The second module is classification, which classifies the kids into two groups, non-dyslexics and suspicious for dyslexia in spelling and reading. A third module is an analysis tool for researchers. The results show that 23% of children were at risk for dyslexia in the training data and 20.7% in the testing data with 98% of accuracy.