Enhancing Dyslexia Awareness: A ML Model for Early Identification and Support

Dyslexia, a developmental disorder marked by challenges in decoding speech sounds and associating them with letters, poses difficulties in early detection. This research employs a comprehensive survey and quiz-based approach, gathering valuable data to form a database. While keeping track of machine learning, we developed a predictive model for dyslexia severity. Early diagnosis is crucial for timely intervention, preventing long-term psychological impacts. The approach not only empowers parents through a user-friendly website but also contributes to the scientific landscape by advancing early dyslexia detection methodologies, optimizing clinical support, and potentially reducing societal costs associated with undiagnosed dyslexia. There are a series of questions that need to be answered by the user, and based on the results, scores are given for language, memory, speed, and visual and audio skills. The objective of this model is to give an accurate prediction of the chances of the user having dyslexia, using the mentioned scores as input. Our research promises practical solutions for improved dyslexia diagnosis and intervention strategies.