랜덤 포레스트 모델을 활용한 국내 청소년 성경험 영향요인 분석 연구: 2019~2021년 청소년건강행태조사 데이터.
Purpose: The objective of this study was to develop a predictive model for the sexual experiences of adolescents using the random forest method and to identify the "variable importance." Methods: The study utilized data from the 2019 to 2021 Korea Youth Risk Behavior Web-based Survey, which included...
| Publicado en: | Journal of Korean Academy of Nursing Vol. 54; no. 2; pp. 193 - 211 |
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| Autores principales: | , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Korean Society of Nursing Science
May2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=177973371&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 177973371 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 20053673 ATS7 jtl: Journal of Korean Academy of Nursing issn: 20053673 maglogo: N pubinfo: dt: May2024 vid: 54 iid: 2 pid: 42744 pub: Korean Society of Nursing Science place: , <Blank> artinfo: ui: 177973371 177973371 177973371 10.4040/jkan.23134 177973371 ppf: 193 ppct: 18 formats: fmt: @attributes: type: P tig: atl: 랜덤 포레스트 모델을 활용한 국내 청소년 성경험 영향요인 분석 연구: 2019~2021년 청소년건강행태조사 데이터. aug: au: 양윤석 권주원 양영란 affil: 전북대학교 공과대학 바이오메디컬공학부, 고령친화복지기기연구센터a sug: subj: Sexuality In Adolescence Life Experiences Prediction Models Human Random Forest Secondary Analysis Surveys South Korea Data Analysis Software Chi Square Test T-Tests Algorithms Adolescent Behavior Risk Taking Behavior Male Female Health Promotion Alcohol Drinking Funding Source Adolescence Adolescent: 13-18 years Male Female ab: Purpose: The objective of this study was to develop a predictive model for the sexual experiences of adolescents using the random forest method and to identify the "variable importance." Methods: The study utilized data from the 2019 to 2021 Korea Youth Risk Behavior Web-based Survey, which included 86,595 man and 80,504 woman participants. The number of independent variables stood at 44. SPSS was used to conduct Rao-Scott χ² tests and complex sample t-tests. Modeling was performed using the random forest algorithm in Python. Performance evaluation of each model included assessments of precision, recall, F1-score, receiver operating characteristics curve, and area under the curve calculations derived from the confusion matrix. Results: The prevalence of sexual experiences initially decreased during the COVID-19 pandemic, but later increased. "Variable importance" for predicting sexual experiences, ranked in the top six, included week and weekday sedentary time and internet usage time, followed by ease of cigarette purchase, age at first alcohol consumption, smoking initiation, breakfast consumption, and difficulty purchasing alcohol. Conclusion: Education and support programs for promoting adolescent sexual health, based on the top-ranking important variables, should be integrated with health behavior intervention programs addressing internet usage, smoking, and alcohol consumption. We recommend active utilization of the random forest analysis method to develop high-performance predictive models for effective disease prevention, treatment, and nursing care. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: Korean refInfo: holdings: @attributes: islocal: N |
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