Autism Spectrum Disorder-Level Prediction and Personalized Education Planning Using TabNet Machine Learning

Researchers developed a machine learning system using TabNet to categorize autism support levels based on student attributes (verbal ability, social interaction, sensory sensitivity, attention span) and recommend tailored educational interventions. The model achieved 99.37% accuracy in autism level classification and integrates predictions into a web-based education planning system for autistic students.