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@article{210317,
        author = {Aman Mishra and Dr. Leena H. Patil and Aditya Singh and Harish Pagarwar and Vranda Rahangdale and Ayush Pawar},
        title = {LDDetect : A Machine Learning Based System for Detection and Classification of Specific Learning Disabilities},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {6},
        pages = {488-492},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=210317},
        abstract = {Learning disabilities such as Dyslexia and Dysgraphia affect 15–20% of students globally, yet remain largely undiagnosed due to costly clinical evaluation and a lack of automated screening tools. Early detection is essential because timely intervention directly improves academic outcomes and long-term learning support. This study reviews existing machine learning–based approaches for learning disability detection and proposes LDDetect, a web-based system for screening Dyslexia and Dysgraphia. The system collects handwriting and reading-assessment data through a web interface, extracts behavioral features, and classifies risk using Random Forest and Support Vector Machine (SVM) classifiers, comparing their performance to select the most accurate model for each disorder, followed by personalized exercise recommendations. The proposed approach is expected to offer an affordable and accessible screening alternative suited to schools and educational environments with limited access to specialists, diagnostic infrastructure or clinical resources.},
        keywords = {Learning Disabilities, Dyslexia, Dysgraphia, Machine Learning, Random Forest, SVM, Early Screening, Web-based Assessment},
        month = {November},
        }