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Identifying the optimal predictors for adolescent mental and physical health using machine learning methods.

Yadi Sun1, Ying Lin2, Henry C Y Ho1

  • 1The Education University of Hong Kong, Hong Kong, China.

Journal of Affective Disorders
|December 26, 2025
PubMed
Summary

Adolescent mental and physical health are influenced by emotional and social capital. Interventions should consider cultural specifics for effective adolescent health strategies.

Keywords:
Adolescent healthCross-society modelMachine learningOptimal predictorsPISA

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Area of Science:

  • Adolescent Health
  • Mental Health
  • Physical Health
  • Cross-cultural Psychology
  • Machine Learning Applications

Background:

  • Adolescent health is a global concern, with prior research often focusing narrowly on limited predictor domains for mental and physical health issues.
  • Existing studies frequently examine single or few predictor domains, potentially overlooking the complex interplay of factors influencing adolescent well-being.

Purpose of the Study:

  • To identify optimal predictors for adolescent mental and physical health using advanced analytical techniques.
  • To examine the influence of a comprehensive set of 46 predictors across nine domains on adolescent health outcomes.
  • To compare predictor importance across different cultural contexts, specifically Hong Kong and the Netherlands.

Main Methods:

  • Utilized multiple machine learning algorithms to analyze a large dataset of adolescent health indicators.
  • Employed data from the Programme for International Student Assessment (PISA), 2022, encompassing adolescents from Hong Kong and the Netherlands.
  • Evaluated the predictive power of 46 variables across nine domains for mental and physical health.

Main Results:

  • Emotional and social capital domains emerged as the most significant predictors for adolescent mental and physical health in both studied societies.
  • Identified culturally nuanced patterns: environmental domain critical in Hong Kong, physical domain critical in the Netherlands.
  • Demonstrated that predictive models trained on data from one society performed less effectively on data from another, highlighting cultural specificity.

Conclusions:

  • Interventions focusing on enhancing emotional and social capital show promise for improving adolescent mental and physical health.
  • Emphasized the necessity of culturally tailored strategies for interventions, acknowledging both overlapping patterns and unique societal influences.
  • Recommended a nuanced approach to adolescent health interventions that respects cultural diversity while leveraging common predictive factors.