Paper Contents
Abstract
Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition affecting communication, social interaction, and behaviour. Early detection of autism is essential for effective therapy and lifelong support. However, traditional diagnostic procedures are often time-consuming, subjective, and dependent on expert interpretation. In this research, a predictive model is developed using machine learning algorithmsprimarily the Random Forest Classifierto estimate the risk of autism based on behavioural and demographic data. The dataset includes responses from autism screening questionnaires (A1A10 behavioural scores), along with demographic indicators such as age, gender, and ethnicity. After pre-processing (encoding, scaling, cleaning),multiple models were trained and evaluated.
Copyright
Copyright © 2025 Sakshi Sameer Tandale. This is an open access article distributed under the Creative Commons Attribution License.
