The Role of Machine Learning in Autism Detection

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that affects social interaction, communication, and behaviour. Early diagnosis is crucial for timely intervention, yet traditional diagnostic methods are subjective, time-consuming, and often inaccessible in remote areas. This research explores the use of machine learning (ML) techniques to enhance autism detection through data-driven approaches. Various supervised learning algorithms, including Support Vector Machines (SVM), Decision Trees, Random Forest, and Deep Learning models, are evaluated using ASD screening datasets. Additionally, we examine unsupervised and reinforcement learning methods to detect ASD traits in children and adults. The results indicate that ML-based models can achieve high accuracy, offering a scalable, cost-effective, and objective alternative to conventional diagnosis methods.