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고은경 ( Goh Eun-Kyoung ) -
전효정 ( Jeon Hyo-Jeong ) -
박현태 ( Park Hyun-Tae ) -
옥수열 ( Ok Soo-Yol ) -
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Abstract
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Purpose: The purpose of this study is to develop a model for predicting mental health risk among adolescentsbased on health behavior information by employing a hybrid machine learning method.
Methods: The studyanalyzed data of 51,850 domestic middle and high school students from 2022 Youth Health Behavior Surveyconducted by the Korea Disease Control and Prevention Agency. Firstly, mental health risk levels (stressperception, suicidal thoughts, suicide attempts, suicide plans, experiences of sadness and despair, loneliness,and generalized anxiety disorder) were classified using the k-mean unsupervised learning technique. Secondly,demographic factors (family economic status, gender, age), academic performance, physical health (body massindex, moderate-intensity exercise, subjective health perception, oral health perception), daily life habits (sleeptime, wake-up time, smartphone use time, difficulty recovering from fatigue), eating habits (consumption ofhigh-caffeine drinks, sweet drinks, late-night snacks), violence victimization, and deviance (drinking, smokingexperience) data were input to develop a random forest model predicting mental health risk, using logistic andXGBoosting. The model and its prediction performance were compared.
Results: First, the subjects wereclassified into two mental health groups using k-mean unsupervised learning, with the high mental health risk groupconstituting 26.45% of the total sample (13,712 adolescents). This mental health risk group included most ofthe adolescents who had made suicide plans (95.1%) or attempted suicide (96.7%). Second, the predictiveperformance of the random forest model for classifying mental health risk groups significantly outperformed thatof the reference model (AUC=.94). Predictors of high importance were ‘difficulty recovering from daytime fatigue’and ‘subjective health perception’.
Conclusion: Based on an understanding of adolescent health behaviorinformation, it is possible to predict the mental health risk levels of adolescents and make interventions in advance.
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KeyWords
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청소년, 정신건강, 건강행태, 하이브리드 머신러닝
Adolescent, Mental health, Health behavior, Hybrid machine learning
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원문 및 링크아웃 정보
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등재저널 정보
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