EARLY DIAGNOSIS OF DYSLEXIA

Abstract

Dyslexia is a common learning disorder that primarily affects reading, writing, and language processing abilities in children. Many individuals with dyslexia remain undiagnosed until later stages of education, which can negatively impact their academic performance, confidence, and overall development. Early identification of dyslexia is crucial for providing timely intervention and personalized learning support. This study proposes an intelligent system for the early diagnosis of dyslexia using machine learning and behavioral pattern analysis. The framework analyzes multiple factors such as reading speed, letter recognition, word comprehension, writing patterns, eye movement behavior, and response time. A hybrid approach combining cognitive assessment data and AI-based prediction models is used to classify children as dyslexic or non- dyslexic. The system employs supervised learning algorithms such as Support Vector Machine (SVM), Random Forest, and Neural Networks to identify patterns associated with dyslexia. Additionally, feature selection techniques are applied to determine the most significant indicators of dyslexia. Experimental results show that the proposed model achieves high accuracy in early detection and can serve as a reliable screening tool for educators and healthcare professionals. Early screening systems based on artificial intelligence can significantly reduce the delay in diagnosing learning disabilities. By combining behavioral indicators with intelligent pattern recognition, the proposed framework enables scalable and cost- effective dyslexia screening in educational environments. Such automated tools can assist teachers and healthcare professionals in identifying at-risk students earlier, allowing them to design personalized learning strategies and intervention programs. Ultimately, the integration of machine learning in educational diagnostics has the potential to improve academic outcomes and reduce the long-term psychological impact associated with undiagnosed learning disorders.