TY - JOUR
KW - Health care
KW - Risk factors
AU - Felipe Mendes Delpino
AU - Alexandre Dias Porto Chiavegatto Filho
AU - Juliana Lustosa Torres
AU - Fabíola Bof de Andrade
AU - Maria Fernanda Lima-Costa
AU - Bruno Pereira Nunes
AB - We 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.
BT - npj Aging
DA - 2025-03-28
DO - 10.1038/s41514-025-00210-7
IS - 1
LA - en
N2 - We 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.
PY - 2025
SP - 1
EP - 8
ST - Predicting all-cause mortality with machine learning among Brazilians aged 50 and over
T2 - npj Aging
TI - Predicting all-cause mortality with machine learning among Brazilians aged 50 and over: results from The Brazilian Longitudinal Study of Ageing (ELSI-Brazil)
UR - https://www.nature.com/articles/s41514-025-00210-7
VL - 11
Y2 - 2025-04-24
SN - 2731-6068
ER -