doi: 10.18178/ijfcc.2023.12.4.609
Using Feature Selection Techniques to Investigate the Myth of Autism Spectrum Disorder
Albert Zheng
1,*
,
,
,
- 1 Academy of the Canyons, Santa Clarita, CA, USA.
- 2 Rancho Cucamonga High School, Rancho Cucamonga, CA, USA.
- 3 Los Osos High School, Rancho Cucamonga, CA, USA.
- 4 California State Polytechnic Univ., Pomona, CA, USA.
Abstract
autism spectrum disorder (ASD) is adevelopmental disorder that affects many people, especiallychildren, with problems in communication and social life.Although many factors such as inherited gene mutation andenvironment influences may play important roles in autism, theactual causes of autism remain as myth. Without proper analysisfor ASD, many people cannot get early detection of ASD and thepublic cannot fully understand ASD. Our goal is, throughscientific investigation, to increase the public awareness onautism, so that better societal support could be provided toindividuals with ASD traits. In this research, we analyze achildren's autism screening dataset, apply feature selectiontechniques to identify key characteristics associated with ASDtraits, and validate our findings with machine learningpredictions. Our research revealed social responsiveness factorsclosely tied to ASD traits, and a strong link between autismassessment questionnaires and ASD traits.
Keywords
- Autism spectrum disorder
- categorical data
- feature selection
- correlation
- chi square test
- mutualinformation
How to 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. https://doi.org/ 10.18178/ijfcc.2023.12.4.609
Copyright & License
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).