Authors : Patrick Ufomba Nwogu; Chigozirim Ajaegbu; Dr. Faruk Umar Ambursa; Dr. Femi Adeluyi

Volume/Issue : Volume 11 - 2026, Issue 9 - September

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Google Scholar : https://tinyurl.com/43s37ad9

DOI : https://doi.org/10.38124/ijisrt/26sep376

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

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Abstract : Dyslexia is a neurodevelopmental learning difficulty that can affect reading, spelling, decoding and academic achievement. Early identification is important because timely intervention can reduce the consequences of delayed recognition. This study investigates feature selection and ensemble machine learning for robust dyslexia prediction using the publicly available behavioural dataset associated with Rello et al. The uploaded Dyt-desktop dataset contains 3,644 observations, 196 predictor attributes and a binary dyslexia outcome.

Keywords : Dyslexia Prediction; Machine Learning; Feature Selection; RF-RFE; Random Forest; XGBoost; Extra Trees; Ensemble Learning; Stacking; Learning Disabilities.

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