Machine Learning Algorithm for Learning Disability Detection and Classifier System

Abstract

The increasing prevalence of learning disabilities among children and adolescents has highlighted the need for early and accurate detection methods. This research presents a comprehensive machine learning-based system designed to detect and classify various types of learning disabilities. The proposed system leverages advanced machine learning algorithms, including decision trees, support vector machines (SVM), and neural networks, to analyze behavioral and academic performance data.The methodology begins with data collection from multiple sources, including standardized tests, teacher assessments, and behavioral observations. This data undergoes preprocessing to handle missing values, normalize features, and select relevant attributes. The system is trained on a labeled dataset, utilizing cross-validation techniques to ensure robustness and avoid overfitting.The core of the system is a multi-stage classifier. The first stage involves a binary classifier that distinguishes between individuals with and without learning disabilities. The second stage comprises a multi-class classifier that categorizes the type of learning disability, such as dyslexia, dysgraphia, dyscalculia, or attention deficit hyperactivity disorder (ADHD).