Developing a Dyslexia Indicator Using Eye Tracking
It addresses the need for cost-effective and accessible diagnostic methods for dyslexia, affecting 10-20% of the global population, though it appears incremental as it builds on existing eye-tracking and machine learning approaches.
This paper tackled the problem of early dyslexia detection by using eye-tracking technology and machine learning, achieving an accuracy of 88.58% with a Random Forest Classifier and identifying varying severity levels through hierarchical clustering.
Dyslexia, affecting an estimated 10% to 20% of the global population, significantly impairs learning capabilities, highlighting the need for innovative and accessible diagnostic methods. This paper investigates the effectiveness of eye-tracking technology combined with machine learning algorithms as a cost-effective alternative for early dyslexia detection. By analyzing general eye movement patterns, including prolonged fixation durations and erratic saccades, we proposed an enhanced solution for determining eye-tracking-based dyslexia features. A Random Forest Classifier was then employed to detect dyslexia, achieving an accuracy of 88.58\%. Additionally, hierarchical clustering methods were applied to identify varying severity levels of dyslexia. The analysis incorporates diverse methodologies across various populations and settings, demonstrating the potential of this technology to identify individuals with dyslexia, including those with borderline traits, through non-invasive means. Integrating eye-tracking with machine learning represents a significant advancement in the diagnostic process, offering a highly accurate and accessible method in clinical research.