Faruk Umar Ambursa

Comparative Analysis of Base Machine-Learning Models for Dyslexia Prediction Using Behavioral Learning Data

Dyslexia is a specific learning disorder that affects reading, decoding, spelling and fluent word recognition. Early identification can facilitate timely educational intervention. This study comparatively evaluates three machine-learning (ML) base classifiers—Random Forest (RF),...

Exploratory Analysis of Ensemble Machine Learning for Dyslexia Detection Using RF-RFE and Stacked Learning

This study develops and empirically evaluates an ensemble machine-learning framework for dyslexia detection using Random-Forest Recursive Feature Elimination (RF-RFE), Random Forest (RF), XGBoost, Extra Trees (ET), and probability-level logistic-regression stacking. The analysis...