  Autism spectrum disorder ASD is a neurodevelopmental disorder associated with
challenges in communication, social interaction, and repetitive behaviors.
Getting a clear diagnosis for a child is necessary for starting early
intervention and having access to therapy services. However, there are many
barriers that hinder the screening of these kids for autism at an early stage
which might delay further the access to therapeutic interventions. One
promising direction for improving the efficiency and accuracy of ASD detection
in toddlers is the use of machine learning techniques to build classifiers that
serve the purpose. This paper contributes to this area and uses the data
developed by Dr. Fadi Fayez Thabtah to train and test various machine learning
classifiers for the early ASD screening. Based on various attributes, three
models have been trained and compared which are Decision tree C4.5, Random
Forest, and Neural Network. The three models provided very good accuracies
based on testing data, however, it is the Neural Network that outperformed the
other two models. This work contributes to the early screening of toddlers by
helping identify those who have ASD traits and should pursue formal clinical
diagnosis.
