The Autism Spectrum Disorder Detection

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition that affects social interaction, communication, and behaviour. Early diagnosis of ASD is crucial for providing timely interventions and support to affected individuals. In this project, we present a novel approach for the detection of ASD using machine learning techniques, implemented in Python. We employed two distinct algorithms, namely the Random Forest Classifier and the Decision Tree Classifier, to analyze a dataset containing 704 records with 21 features. The dataset includes a diverse range of attributes, such as sensory perception, cognitive abilities, demographics, and medical history, which are potentially indicative of ASD. Our model's performance on this dataset is a testament to the power of machine learning in healthcare applications.