Advance Machine Learning Methods for Dyslexia Biomarker Detection

Dyslexia is a neurological disease that greatly impairs reading and comprehension ability, notably in school-aged children, with a higher prevalence in boys. This syndrome can result in poor academic achievement and long-term effects on self-esteem. To improve dyslexia detection, this study investigates the application of the Random Forest method, a machine learning technique noted for its durability and accuracy. The Random Forest algorithm is used to classify persons with dyslexia using a dataset of brain and behavioral markers. The goal is to uncover patterns and biomarkers that can aid in early detection. This strategy addresses major issues in dyslexia diagnosis, such as the necessity for interpretable biomarkers and the risk of overfitting, by taking advantage of Random Forests' ensemble nature to improve model reliability and generalization. The results of this study show that the Random Forest algorithm has the capacity to detect dyslexia with clinically significant accuracy, making it a promising tool for early intervention and support.