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머신러닝 분석을 활용한 초등학교 1학년 ADHD 위험군아동 종단 예측모형 개발Development of a Machine-Learning Predictive Model for First-Grade Children at Risk for ADHD

Other Titles
Development of a Machine-Learning Predictive Model for First-Grade Children at Risk for ADHD
Authors
이동미Park, Ju Hee장혜인김호정배진
Issue Date
Oct-2021
Publisher
한국보육지원학회
Citation
한국보육지원학회지, v.17, no.5, pp.83 - 103
Journal Title
한국보육지원학회지
Volume
17
Number
5
Start Page
83
End Page
103
URI
https://yscholarhub.yonsei.ac.kr/handle/2021.sw.yonsei/5360
DOI
10.14698/jkcce.2021.17.05.083
ISSN
1738-9496
Abstract
Objective: This study aimed to develop a longitudinal predictive model that identifies first-grade children who are at risk for ADHD and to investigate the factors that predict the probability of belonging to the at-risk group for ADHD by using machine learning. Methods: The data of 1,445 first-grade children from the 1st, 3rd, 6th, 7th, and 8th waves of the Korean Children’s Panel were analyzed. The output factors were the at-risk and non-risk group for ADHD divided by the CBCL DSM-ADHD scale. Prenatal as well as developmental factors during infancy and early childhood were used as input factors. Results: The model that best classifies the at-risk and the non-risk group for ADHD was the LASSO model. The input factors which increased the probability of being in the at-risk group for ADHD were temperament of negative emotionality, communication abilities, gross motor skills, social competences, and academic readiness. Conclusion/Implications: The outcomes indicate that children who showed specific risk indicators during infancy and early childhood are likely to be classified as being at risk for ADHD when entering elementary schools. The results may enable parents and clinicians to identify children with ADHD early by observing early signs and thus provide interventions as early as possible.
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