Comparative diagnostic performance of machine learning models and traditional scores for HFpEF in older adults
AIMS: Diagnosing heart failure with preserved ejection fraction (HFpEF) remains challenging, particularly in older individuals. We hypothesized that machine learning (ML) approaches could improve diagnostic accuracy compared with HFpEF scores.METHODS: We evaluated the diagnostic performance of four supervised ML algorithms (random forest [RF], extreme gradient boosting [XGBoost], support vector ma
