Key result
XGBoost shows no advantage over logistic regression for predicting physical inactivity.
Why the study?
Physical inactivity is a major modifiable risk factor with marked disparities, making robust and interpretable predictors essential for public health strategies.
Does an explainable machine learning approach (XGBoost) improve the prediction of physical inactivity compared to survey-weighted logistic regression in Chilean adults?
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.
Logistic regression suffices for physical inactivity screening in Chilean adults; leaves open meaningful gains from explainable ML in CV risk profiling.
Physical inactivity remains a major modifiable risk factor for non-communicable diseases and continues to exhibit marked socioeconomic and gender disparities in Latin America. Identifying robust and interpretable predictors of inactivity in nationally representative datasets is essential for informing public health strategies. This study compared a survey-weighted logistic regression model and an explainable machine learning approach (XGBoost) to predict physical inactivity among Chilean adults using data from the 2024 National Physical Activity and Sports Survey (ENAFyD; n = 5248). Models were evaluated on a stratified held-out test set (n = 1050) using weighted and unweighted area under the ROC curve (AUC), Brier scores, and calibration curves. Survey-weighted logistic regression achieved a weighted AUC of 0.801, while XGBoost achieved 0.797, demonstrating comparable discrimination. XGBoost showed marginally lower Brier scores, indicating slightly improved probabilistic calibration. Low socioeconomic status, female sex, lower monthly physical activity expenditure, limited facility access, and lower engagement with digital resources were consistently associated with higher inactivity risk. SHAP-style contribution analysis provided additional insight into feature-level influence within the machine learning framework. Overall, both approaches demonstrated similar predictive capacity, supporting the complementary use of classical regression and explainable machine learning for population-level physical inactivity research.
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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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