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June 27, 2026Discover PsychologyOpen Access

Using explainable machine learning to classify subjective wellbeing status in Chilean adolescents

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Authors

SFSergio Fuentealba-UrraCCCristián CéspedesARAndrés Rubio

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Overview

Randomized trial evaluates explainable AI models for classifying wellbeing in Chilean adolescents, suggesting effective prediction methods.

Key Points

  • This research aims to classify subjective well-being status in adolescents using machine learning models and identify significant predictors.
  • Cross-sectional sample of 913 students aged 10-19 from public schools in Biobío region.
  • Utilized the Personal Wellbeing Index – School Children (PWI-SC) for wellbeing assessment.
  • Employed XGBoost and SHAP for model prediction and interpretation.
  • XGBoost achieved an AUC of 0.86 for classifying high versus low subjective wellbeing.
  • Key predictors included depressive symptoms, emotional regulation, healthy eating, and physical activity.
  • Findings indicate predictable associations rather than causation regarding adolescent wellbeing.

Cite This Study

Fuentealba-Urra et al. (2026) studied this question.

synapsesocial.com/papers/6a3f6871aea7db3c1953f7d5https://doi.org/10.1007/s44202-026-00773-w
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  5. 5Predicting poor health-related quality of life among Chinese adolescents using explainable machine learning: the role of school adjustment, family context, and lifestyle factors2026