Predictive factors of adolescents' happiness: a random forest analysis of the 2023 Korea Youth Risk Behavior Survey.
Purpose: This study aimed to identify predictive factors affecting adolescents' subjective happiness using data from the 2023 Korea Youth Risk Behavior Survey. A random forest model was applied to determine the strongest predictive factors, and its predictive performance was compared with traditiona...
| Publicado en: | Child Health Nursing Research Vol. 31; no. 2; pp. 85 - 96 |
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| Autores principales: | , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Child Health Nursing Research
Apr2025
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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=184817583&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184817583 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 22879110 FUY5 jtl: Child Health Nursing Research issn: 22879110 maglogo: N pubinfo: dt: Apr2025 vid: 31 iid: 2 pid: 72188 pub: Child Health Nursing Research artinfo: ui: 184817583 184817583 184817583 10.4094/chnr.2024.049 184817583 ppf: 85 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Predictive factors of adolescents' happiness: a random forest analysis of the 2023 Korea Youth Risk Behavior Survey. aug: au: Kim, Eun Joo Kim, Seong Kwang Jung, Seung Hye Ryu, Yo Seop affil: Associate Professor, Department of Nursing, Gangneung-Wonju National University, Wonju, Korea sug: subj: Happiness Evaluation Adolescent Behavior Evaluation Machine Learning Utilization Prediction Models Human Funding Source South Korea Male Female Adolescence Random Forest Surveys Regression Comparative Studies Secondary Analysis Cross Sectional Studies Descriptive Statistics Confidence Intervals Adolescent Health Adolescent: 13-18 years Male Female ab: Purpose: This study aimed to identify predictive factors affecting adolescents' subjective happiness using data from the 2023 Korea Youth Risk Behavior Survey. A random forest model was applied to determine the strongest predictive factors, and its predictive performance was compared with traditional regression models. Methods: Responses from a total of 44,320 students from grades 7 to 12 were analyzed. Data pre-processing involved handling missing values and selecting variables to construct an optimal dataset. The random forest model was employed for prediction, and SHAP (Shapley Additive Explanations) analysis was used to assess variable importance. Results: The random forest model demonstrated a stable predictive performance, with an R2 of.37. Mental and physical health factors were found to significantly affect subjective happiness. Adolescents' subjective happiness was most strongly influenced by perceived stress, perceived health, experiences of loneliness, generalized anxiety disorder, suicidal ideation, economic status, fatigue recovery from sleep, and academic performance. Conclusion: This study highlights the utility of machine learning in identifying factors influencing adolescents' subjective happiness, addressing limitations of traditional regression approaches. These findings underscore the need for multidimensional interventions to improve mental and physical health, reduce stress and loneliness, and provide integrated support from schools and communities to enhance adolescents' subjective happiness. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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