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October 9, 20250 citationsOpen Access

Key Determinants of Subjective Well-Being in Taiwan Using Machine Learning

Determinants of Subjective Well-Being in Taiwan: A Machine Learning and SHAP Analysis

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Authors

MYMing‐Chin YehCKChun-Tung KuoSHShu-Hui Hsieh

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Overview

Cross-sectional analysis reveals determinants of subjective well-being, suggesting actionable improvements in social relationships and health.

Key Points

  • Identified key determinants of subjective well-being include family, health, and financial stability, with varying impacts.
  • Machine learning models, particularly gradient boosting, showed superior predictive performance for subjective well-being outcomes.
  • Non-linear associations indicate threshold effects, where subjective well-being sharply increases beyond certain satisfaction levels.
  • Protective factors such as interpersonal relationships demonstrate significant importance, especially for those in lower baseline conditions.

Cite This Study

Yeh et al. (2025) studied this question.

synapsesocial.com/papers/68e70da790569dd607ee5a91https://doi.org/10.1101/2025.10.05.25337343
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Predictors of subjective well-being in Taiwan: a machine learning analysis with SHAP explanations2026 · 1 citations
  2. 2Using explainable machine learning to classify subjective wellbeing status in Chilean adolescents2026
  3. 3Developing a machine learning‐based instrument for subjective well‐being assessment on Weibo and its psychological significance: An evaluative and interpretive research2024 · 7 citations
  4. 4Income, psychological security, and subjective well-being in urban China: a machine learning analysis with SHAP interpretation2025
  5. 5Income, psychological security, and subjective well-being in urban China: a machine learning analysis with SHAP interpretation2025