Survey On: Early Detection & Analysis Of Autism Spectrum Disorder Using Machine Learning

Abstract

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that affects communication, social interaction, and behavior. Early detection of ASD is crucial for providing timely intervention and improving the quality of life for affected individuals. However, traditional diagnostic methods are often time-consuming, subjective, and require expert involvement, which may not always be accessible. This project proposes an intelligent system for the early detection of Autism Spectrum Disorder using machine learning techniques. The system utilizes questionnaire-based datasets and behavioral features to classify individuals as ASD-positive or ASD-negative. Various preprocessing techniques are applied to clean and normalize the data, followed by the implementation of classification algorithms to achieve accurate predictions. The developed system provides a user-friendly interface where users can input responses and receive instant predictions. By leveraging machine learning, the model enhances diagnostic efficiency and reduces dependency on manual evaluation. This approach aims to support healthcare professionals and caregivers in early screening while ensuring accuracy and reliability.