Tuna G.Tuna A.2024-06-122024-06-1220219.7818E+12https://doi.org/10.4018/978-1-7998-7732-5.ch001https://hdl.handle.net/20.500.14551/16765Autism spectrum disorder (ASD) is a challenging developmental condition that involves restricted and/or repetitive behaviors and persistent challenges in social interaction and speech and nonverbal communication. There is not a standard medical test used to diagnose ASD; therefore, diagnosis is made by looking at the child’s developmental history and behavior. In recent years, due to the increase in diagnosed cases of ASD, researchers proposed software-based tools to aid in and expedite the diagnosis. Considering the fact that most of these tools rely on the use of classifiers, in study, random forest, decision tree, k-nearest neighbors, and zero rule algorithms are used as classifiers, and their performances are compared using well-known performance metrics. As proven in the study, random forest algorithm can provide higher accuracy than the others in the classification of ASD and can be integrated into a computer- or humanoid-robot-based system for automated prescreening and diagnosis of ASD in preschool children groups. © 2022 by IGI Global.en10.4018/978-1-7998-7732-5.ch001info:eu-repo/semantics/closedAccess[Abstarct Not Available]Book Chapter1212-s2.0-85129855285N/A