• Jan 04, 2024 News!IJFCC will adopt Article-by-Article Work Flow
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  • Dec 05, 2023 News!Vol.12, No.4 has been published with online version.   [Click]
General Information
    • ISSN: 2010-3751 (Print)
    • Frequency: Quarterly
    • DOI: 10.18178/IJFCC
    • Editor-in-Chief: Prof. Pascal Lorenz
    • Executive Editor: Ms. Tina Yuen
    • Abstracting/ Indexing: Crossref, Electronic Journals LibraryINSPEC(IET), Google Scholar, EBSCO, etc.
    • E-mail:  ijfcc@ejournal.net 
    • Article Processing Charge: 500 USD
Editor-in-chief

Prof. Pascal Lorenz
University of Haute Alsace, France
 
It is my honor to be the Editor-in-Chief of IJFCC. The journal publishes good papers in the field of future computer and communication. Hopefully, IJFCC will become a recognized journal among the readers in the filed of future computer and communication.

IJFCC 2023 Vol.12(4): 93-99
DOI: 10.18178/ijfcc.2023.12.4.609

Using Feature Selection Techniques to Investigate the Myth of Autism Spectrum Disorder

Albert Zheng*, He Zhu, Xinyi Hu, and Lan Yang

Abstract—autism spectrum disorder (ASD) is a developmental disorder that affects many people, especially children, with problems in communication and social life. Although many factors such as inherited gene mutation and environment influences may play important roles in autism, the actual causes of autism remain as myth. Without proper analysis for ASD, many people cannot get early detection of ASD and the public cannot fully understand ASD. Our goal is, through scientific investigation, to increase the public awareness on autism, so that better societal support could be provided to individuals with ASD traits. In this research, we analyze a children's autism screening dataset, apply feature selection techniques to identify key characteristics associated with ASD traits, and validate our findings with machine learning predictions. Our research revealed social responsiveness factors closely tied to ASD traits, and a strong link between autism assessment questionnaires and ASD traits.

Index Terms—Autism spectrum disorder, categorical data, feature selection, correlation, chi square test, mutual information

Albert Zheng is with the Academy of the Canyons, Santa Clarita, CA, USA.
He Zhu is with the Rancho Cucamonga High School, Rancho Cucamonga, CA, USA.
Xinyi Hu is with the Los Osos High School, Rancho Cucamonga, CA, USA.
Lan Yang, is with the California State Polytechnic Univ., Pomona, CA, USA.
*Correspondence: anpei.zheng@gmail.com (A.Z.)

[PDF]

Cite: Albert Zheng, He Zhu, Xinyi Hu, and Lan Yang, "Using Feature Selection Techniques to Investigate the Myth of Autism Spectrum Disorder," International Journal of Future Computer and Communication, vol. 12, no. 4, pp. 93-99, 2023.


Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0)

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