Prevalent self-reported CVD

logistic regression predicts prevalent self-reported CVD in silico (ROC AUC 0.669, PR AUC 0.154).

“logistic regression achieved 0.669, 0.154, and 0.0694.”

In silicon = 187,791

Benchmarking machine learning for cardiovascular disease classification across 46 countries with geographic and temporal transportability assessment

Research Square·14 Oct 2026Preprint

XGBoost predicts prevalent self-reported CVD in silico (ROC AUC 0.671, PR AUC 0.162).

“XGBoost achieved ROC AUC 0.671, PR AUC 0.162, and Brier score 0.0692; logistic regression achieved 0.669, 0.154, and 0.0694.”

In silicon = 187,791

Benchmarking machine learning for cardiovascular disease classification across 46 countries with geographic and temporal transportability assessment

Research Square·14 Oct 2026Preprint