Early Detection and Support for Learning Disabilities: A Machine Learning Approach Empowering Educators

Learning disabilities among students present significant challenges for educators and specialists in delivering timely support and interventions. This project aims to develop a machine learning-based system for early detection and support of students with learning disabilities. We collect and preprocess a comprehensive dataset, including academic records, assessments, cognitive test results, and behavioral observations. We employ feature engineering techniques to extract meaningful features indicative of learning disabilities. We compare various machine learning methods, including support vector machines, decision trees, random forests, logistic regression, and k-nearest neighbors, to identify the most suitable model. The deployed system offers real-time predictions and interventions for educators and specialists, facilitating early identification of students at risk of learning disabilities. The decision tree proves effective in predicting the presence of dyslexia, achieving an accuracy of 88.94%. We recommend personalized support strategies tailored to each student's needs to promote inclusive learning environments. A user-friendly interface with visualizations enhances data interpretation for educators and specialists. Continuous evaluation and improvement ensure the system's effectiveness and adaptability. This system has the potential to significantly impact the field of education by empowering educators to support students with learning disabilities and promote equal educational opportunities for all