Key result
Survey-weighted logistic regression and XGBoost achieved comparable predictive capacity for physical inactivity in Chilean adults, with weighted AUCs of 0.801 and 0.797, respectively.
Why the study?
Does an explainable machine learning approach (XGBoost) improve the prediction of physical inactivity compared to survey-weighted logistic regression in Chilean adults?
Population
5,248 Chilean adults from the 2024 National Physical Activity and Sports Survey (ENAFyD)
Comparison
Explainable machine learning approach for… vs Survey-weighted logistic regression model
Design
Cross-sectional
Authors
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Both survey-weighted logistic regression and explainable machine learning (XGBoost) demonstrated similar predictive capacity for identifying physical inactivity risk factors in a nationally representative Chilean cohort.
Does an explainable machine learning approach (XGBoost) improve the prediction of physical inactivity compared to survey-weighted logistic regression in Chilean adults?
Both survey-weighted logistic regression and explainable machine learning (XGBoost) demonstrated similar predictive capacity for identifying physical inactivity risk factors in a nationally representative Chilean cohort.
Souza-Lima et al. (2026) studied this question. Survey-weighted logistic regression and XGBoost achieved comparable predictive capacity for physical inactivity in Chilean adults, with weighted AUCs of 0.801 and 0.797, respectively.
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