An ensemble features aware machine learning model for detection and staging of dyslexia

Abstract

Dyslexia is a specific learning disorder (SLD) which may affect young child's cognitive skills, text comprehension, reading-writing and also problemsolving abilities. To diagnose and identify dyslexia, the testing scale tool has been proposed using artificial intelligence technique. The proposed tool allows the student who is suspected to have dyslexia to take up quiz and perform certain task based on the type of learning impairments. After completion of the test, resultant data is provided as input to the proposed ensemble feature aware machine-learning (EFAM) XGBoost (XGB) model. Based on the student assessment score and time taken by children, the EFAMXGB algorithm predicts dyslexia. The proposed EFAM-XGB is used to develop an integrated and user-friendly tool that is highly accurate in identifying reading disorders even with presence of realistic imbalanced dataset and suggest the most appropriate instructional activities to parents and teachers. The EFAM-XGB-based dyslexia detection method achieves very good accuracy of 98.7% for dyslexia dataset; thus, attain better performance in comparison with existing machine learning (ML)-based methodologies.