Heart failure & cardiomyopathies
Comparative diagnostic performance of machine learning models and traditional scores for heart failure with preserved ejection fraction (HFpEF) in older adults
Authors' conclusion
Machine learning algorithms, particularly RF and XGBoost, demonstrated superior diagnostic accuracy compared to established HFpEF scoring systems.
Study context
- Population
- Individuals aged 60 to 80 years; HFpEF cohorts and community-based cohorts without HFderivation N = 1474
- Participants
- 2,017 individuals
- Comparison
- Machine learning (ML) models vs HFpEF scores
Methodological detailsderivation and independent validation cohorts
- Derivation and independent validation cohorts
Performance
As reported in the abstract
Diagnostic performance for HFpEF (AUC)
RF
0.98
AUC
XGBoost
0.96
AUC
HFA-PEFF
0.86
AUC
H2FPEF
0.79
AUC
ΔC-index
- RF vs H2FPEF
- +0.20
- XGBoost vs H2FPEF
- +0.18
- RF vs HFA-PEFF
- +0.12
- XGBoost vs HFA-PEFF
- +0.10
- RF vs HFpEF-ABA
- +0.17
- XGBoost vs HFpEF-ABA
- +0.15
Further findings
Most influential feature (RF and XGBoost)
- Elevated natriuretic peptides, share of model explainability
- 36%
Original publication
Comparative diagnostic performance of machine learning models and traditional scores for HFpEF in older adults. Eur J Heart Fail. Published 20 March 2026. doi:10.
Summarised from the published abstract; every number is checked against it