AN INVESTIGATION INTO PREDICTIVE MODELS FOR EARLY DETECTION MACHINE LEARNING BASED DYSLEXIA PREDICTION

Abstract

A neurological condition called dyslexia is typified by a lack of precision in word comprehension and generally subpar reading abilities. It impacts a sizable portion of school-age children, more often affecting boys, and puts them at risk for lifelong low self-esteem and subpar academic performance. The ultimate goal is to develop a dyslexia diagnostic approach based on brain biomarkers. Numerous machine learning techniques and, more recently, deep learning techniques have been applied to diverse dataset types with above-chance classification performance in this regard. This tutorial provides a thorough resource for both inexperienced and seasoned developers, outlining a methodical approach to creating a Dyslexia Detection Web App using Flask and Boosting techniques. The abstract summarizes two main points of emphasis: using sophisticated Boosting techniques to identify dyslexia accurately and utilizing Flask's flexibility to create web apps quickly and easily. Through an exploration of the complexities involved in dyslexia identification and the provision of useful insights regarding algorithm implementation, the goal of this book is to enable readers to develop an advanced tool for supporting dyslexics. The guide's dedication to increasing accessibility, cultivating inclusion through technology, and expanding the field of assistive applications is emphasized in the abstract.