Implementation of various AI techniques for Autism Spectrum Disorder Detection

A complex neurological disorder affecting behavior, communication, and social interactions is known as autism spectrum disorder (ASD). For people with ASD, improving outcomes requires primordial detection and intervention. The diagnosis of ASD has been transformed by the combination of AI and ML, particularly in young children. In order to create reliable autism screening tools, researchers have explored a variety of machine learning algorithms, including Decision Trees, XGBClassifier, Random Forest, AdaBoost Classifier, CatBoost Classifier, KMeans to Artificial Neural Networks (ANN). Artificial Neural Networks, energised by the intricacies of the human brain, stand out for their remarkable ability to decipher complex patterns within data, thus proving effective in ASD prediction tasks. Nonetheless, ANN models often necessitate substantial data and computational resources for training. It is imperative to undertake rigorous comparative analyses based on accurate and efficient results to determine the most suitable algorithm for ASD prediction. Furthermore, continuous refinement and validation of these varied algorithms along with the representative and comprehensive datasets are vital to ensure their factuality and applicability in real world problems.