Exploring and Identifying Key Factors in Predicting Dyslexia in Children: Advanced Machine Learning Algorithms From Screening to Diagnosis

This study demonstrated the transformative potential of ML in dyslexia diagnostics. By systematically prioritising phonological awareness, RAN and attention deficits, ML models offer a scalable, objective framework for early identification. These tools could alleviate reliance on subjective assessments, enabling timely interventions to mitigate dyslexia's long-term impacts.