{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T16:34:45Z","timestamp":1783528485769,"version":"3.55.0"},"reference-count":29,"publisher":"Korean Academy of Child Health Nursing","issue":"2","license":[{"start":{"date-parts":[[2025,4,30]],"date-time":"2025-04-30T00:00:00Z","timestamp":1745971200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/3.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2021R1A2C1095530"],"award-info":[{"award-number":["2021R1A2C1095530"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["e-chnr.org"],"crossmark-restriction":true},"short-container-title":["Child Health Nurs Res"],"published-print":{"date-parts":[[2025,4,30]]},"abstract":"<jats:p>Purpose: This study aimed to identify predictive factors affecting adolescents\u2019 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\u2019 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\u2019 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\u2019 subjective happiness.<\/jats:p>","DOI":"10.4094\/chnr.2024.049","type":"journal-article","created":{"date-parts":[[2025,4,30]],"date-time":"2025-04-30T07:44:09Z","timestamp":1745999049000},"page":"85-95","update-policy":"https:\/\/doi.org\/10.4094\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Predictive factors of adolescents\u2019 happiness: a random forest analysis of the 2023 Korea Youth Risk Behavior Survey"],"prefix":"10.4094","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1554-3927","authenticated-orcid":true,"given":"Eun 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