Screening For Childhood Autism Spectrum Disorder Using Machine Learning

Autism Spectrum Disorder (ASD) affects communication, behavior, and social interactions, and early detection is crucial for intervention. Current diagnostic method sare subjective and prone to delays or misdiagnosis. This project explores machine learning (ML) to automate ASD screening using behavioral data, diagnostic questionnaires, and developmental milestones. Supervised learning algorithms classify children into ASD or non-ASD categories with high accuracy. The approach aims to assist clinicians in early identification and intervention. It offers a scalable, efficient, and objective screening tool for ASD in clinical and educational settings.