Transforming Education And Therapy For Children On The Autism Spectrum with Machine Learning Solutions

Abstract

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that affects cognitive, social, behavioral, and sensory development. Early diagnosis and intervention are crucial but remain challenging due to cultural, environmental, and diagnostic limitations, particularly in Sri Lanka. This research proposes a machine learning-driven web-based system to assess and support children with ASD across four critical domains: behavioral observation, cognitive skills, social skills, and sensory processing. By integrating technologies such as deep learning, computer vision, and natural language processing, the system utilizes eye-tracking, facial expression analysis, and real-time video monitoring to identify developmental challenges. Additionally, culturally adaptive parental questionnaires and interactive learning activities enhance the accuracy of ASD assessments and provide personalized intervention recommendations. The proposed approach bridges gaps in early ASD detection by offering a scalable, accessible, and contextually relevant solution for Sri Lanka. Experimental results show high accuracy in behavioral (90%), cognitive (92%), social (92%), and sensory (94%) models. This scalable, accessible solution bridges gaps in early ASD detection, offering a culturally relevant tool for families and healthcare providers in Sri Lanka. The system empowers caregivers with real-time insights and tailored interventions, improving the quality of life for children with ASD and their families, and advancing inclusive support systems globally.