Autism Spectrum Disorder Prediction Using Machine Learning

Abstract

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by challenges in social interaction, communication, and restricted repetitive behaviors. Early diagnosis and intervention significantly improve outcomes for individuals with ASD. In this paper, we propose a machine learning approach using an Artificial Neural Network (ANN) classifier to predict ASD based on a set of relevant features extracted from clinical assessments and behavioral observations. The ANN model is trained on a large dataset of individuals with and without ASD, incorporating features such as demographic information, medical history, and behavioral characteristics. Moreover, its web-based deployment ensures broader accessibility, facilitating early interventions and support. These advanced models can identify subtle patterns that may not be detectable through traditional clinical assessments alone.