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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

Gurkan Tuna, Ayşe Tuna
Copyright: © 2022 |Pages: 21
ISBN13: 9781799877325|ISBN10: 1799877329|ISBN13 Softcover: 9781799877332|EISBN13: 9781799877349
DOI: 10.4018/978-1-7998-7732-5.ch001
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MLA

Tuna, Gurkan, and Ayşe Tuna. "Screening and Diagnosis of Autism Spectrum Disorder via Assistive Tools: Classification Algorithms." Understanding Parent Experiences and Supporting Autistic Children in the K-12 School System, edited by Jillian Yarbrough, IGI Global, 2022, pp. 1-21. https://doi.org/10.4018/978-1-7998-7732-5.ch001

APA

Tuna, G. & Tuna, A. (2022). Screening and Diagnosis of Autism Spectrum Disorder via Assistive Tools: Classification Algorithms. In J. Yarbrough (Ed.), Understanding Parent Experiences and Supporting Autistic Children in the K-12 School System (pp. 1-21). IGI Global. https://doi.org/10.4018/978-1-7998-7732-5.ch001

Chicago

Tuna, Gurkan, and Ayşe Tuna. "Screening and Diagnosis of Autism Spectrum Disorder via Assistive Tools: Classification Algorithms." In Understanding Parent Experiences and Supporting Autistic Children in the K-12 School System, edited by Jillian Yarbrough, 1-21. Hershey, PA: IGI Global, 2022. https://doi.org/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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