Enhancing Early Autism Spectrum Disorder Detection Using Machine Learning : A Data-Driven Approach for Toddlers and Adults

A Neurodevelopmental disorder named Autism Spectrum Disorder (ASD) presents with difficulties in social interaction and causes people to repeat specific behaviors. Early detection of ASD remains essential for proper intervention since current diagnostic approaches require time-intensive expert evaluation methods. A new research study demonstrates how Machine Learning (ML) technology identifies ASD in toddlers and adults with great efficiency through non-invasive methods that scale very well. The implementation of preselection techniques based on analysis of variance (ANOVA) and principal components analysis (PCA) enhances both model performance and the ability to identify significant diagnostic markers. Multiple ML algorithms receive thorough evaluations with optimal data splits to guarantee their reliability and stability throughout different operational conditions. Our method shows promise to enhance ASD diagnosis through faster identification while addressing screening obstacles together with obstacles in healthcare accessibility. These findings showcase how ML technology revolutionizes healthcare because it delivers swift and exact ASD diagnosis which provides advantages to entire communities of patients together with their families and medical professionals.