Predictive factors of adolescents’ happiness: a random forest analysis of the 2023 Korea Youth Risk Behavior Survey
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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 per- formance 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 con- struct 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 R of .37. Mental and physical health factors were found to significantly affect subjec-2tive happiness. Adolescents’ subjective happiness was most strongly influenced by per- ceived stress, perceived health, experiences of loneliness, generalized anxiety disorder, suicidal ideation, economic status, fatigue recovery from sleep, and academic perfor- mance. Conclusion: This study highlights the utility of machine learning in identifying factors influencing adolescents’ subjective happiness, addressing limitations of traditional re- gression approaches. These findings underscore the need for multidimensional interven- tions to improve mental and physical health, reduce stress and loneliness, and provide integrated support from schools and communities to enhance adolescents’ subjective happiness.
Abstract
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 per- formance 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 con- struct 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 R of .37. Mental and physical health factors were found to significantly affect subjec-2tive happiness. Adolescents’ subjective happiness was most strongly influenced by per- ceived stress, perceived health, experiences of loneliness, generalized anxiety disorder, suicidal ideation, economic status, fatigue recovery from sleep, and academic perfor- mance. Conclusion: This study highlights the utility of machine learning in identifying factors influencing adolescents’ subjective happiness, addressing limitations of traditional re- gression approaches. These findings underscore the need for multidimensional interven- tions to improve mental and physical health, reduce stress and loneliness, and provide integrated support from schools and communities to enhance adolescents’ subjective happiness.