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Screening and Diagnosis of Autism Spectrum Disorder via Assistive Tools: Classification Algorithms

Screening and Diagnosis of Autism Spectrum Disorder via Assistive Tools: Classification Algorithms
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Author(s): Gurkan Tuna (Trakya University, Turkey) and Ayşe Tuna (Trakya University, Turkey)
Copyright: 2022
Pages: 21
Source title: Understanding Parent Experiences and Supporting Autistic Children in the K-12 School System
Source Author(s)/Editor(s): Jillian Yarbrough (West Texas A&M University, USA)
DOI: 10.4018/978-1-7998-7732-5.ch001

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Abstract

Autism 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.

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