01805nas a2200241   4500000000100000000000100001008004100002260001500043653001600058653001700074100002600091700004300117700002700160700002800187700003000215700002400245245016200269856005500431300000800486490000700494520104800501022001401549       2025                            d  c2025-03-2810aHealth care10aRisk factors1 aFelipe Mendes Delpino1 aAlexandre Dias Porto Chiavegatto Filho1 aJuliana Lustosa Torres1 aFabíola Bof de Andrade1 aMaria Fernanda Lima-Costa1 aBruno Pereira Nunes00aPredicting all-cause mortality with machine learning among Brazilians aged 50 and over: results from The Brazilian Longitudinal Study of Ageing (ELSI-Brazil)  uhttps://www.nature.com/articles/s41514-025-00210-7  a1-80 v113 aWe aimed to develop a machine-learning model to predict all-cause mortality among Brazilians aged 50 and over, incorporating demographic, health, and lifestyle characteristics as predictors. We analyzed data from the Brazilian Longitudinal Study of Aging (ELSI-Brazil), waves 1 and 2 (2015–2021), a nationally representative sample from 70 municipalities across Brazil’s five regions. Nine algorithms, including Random Forest, Gradient Boosting, XGBOOST, and Logistic Regression, were tested on 9412 participants (54.6% female), with 970 deaths recorded over approximately five years. Using 59 predictor variables, we assessed performance with metrics like AUC, accuracy, precision, and F1-Score. Random Forest excelled with an AUC of 0.92 (95% CI: 0.90–0.94). SHAP analysis highlighted age, sex, BMI, medication use, and physical activity as top predictors. Integrating these models into healthcare systems can improve policy planning and enable targeted interventions, ultimately fostering better health outcomes for aging populations.  a2731-6068