Autism Spectrum Disorder Detection in ML

Abstract

Autism Spectrum is a neuro-developmental disorder. The ASD (Autism Spectrum Disorder) Screening Tool project is developed to help screen individuals for potential ASD traits. This tool asks questions based on the user's age group and uses a pre-trained model to predict ASD traits. It also provides resources and recommendations based on the screening results. We gathered early-detected ASD datasets relating to toddlers, children, adolescents, and adults and applied several feature transformation methods to these datasets. Various classification techniques were then implemented with these transformed ASD datasets and assessed for their performance. Here, the Random Forest Classifier predicts output and suggests recovery strategies, including videos and yoga, to improve brain health. The results show that when machine learning methods are carefully enhanced, they can accurately predict whether someone has Autism Spectrum Disorder (ASD). This means we can use these models effectively in real-life situations.