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 machines, and decision trees) to identify HFpEF in individuals aged 60 to 80 years. The models were trained on three derivation cohorts (N = 1474; HFpEF: KaRen, MEDIA cohorts; community-based without HF: Malmö Preventive Project) and validated in two independent cohorts (N = 542; HFpEF: HF-Nancy cohort; community-based without HF: STANISLAS cohort). Performance metrics included accuracy, F-measure, area under the receiver operating characteristic curve (AUC), and C-index. ML models were also compared with HFA-PEFF, H2FPEF, and HFpEF-ABA scores.
RESULTS: Among 2017 participants, RF and XGBoost demonstrated the highest diagnostic value, outperforming traditional HFpEF scores (AUC: RF, 0.98; XGBoost, 0.96; HFA-PEFF, 0.86; H2FPEF, 0.79). RF and XGBoost also showed the greatest gain in discriminative capacity among ML algorithms when compared with H2FPEF (ΔC-index: RF +0.20, XGBoost +0.18), HFA-PEFF (ΔC-index: RF +0.12, XGBoost +0.10), and HFpEF-ABA score (ΔC-index: RF +0.17, XGBoost +0.15). Elevated natriuretic peptides were by far the most influential feature in both RF and XGBoost models (36% of model explainability).
CONCLUSIONS: Machine learning algorithms, particularly RF and XGBoost, demonstrated superior diagnostic accuracy compared to established HFpEF scoring systems. These findings support the potential integration of ML-based tools into clinical workflows to facilitate earlier identification of HFpEF and prompt initiation of guideline-recommended therapies.
}}, author = {{Monzo, Luca and Huttin, Olivier and Bresso, Emmanuel and Duarte, Kevin and Linde, Cecilia and Lund, Lars H and Hage, Camilla and Donal, Erwan and Magnusson, Martin and Nilsson, Peter and Leosdottir, Margret and Bozec, Erwan and Baudry, Guillaume and Zannad, Faiez and Girerd, Nicolas}}, issn = {{1879-0844}}, language = {{eng}}, month = {{03}}, publisher = {{Elsevier}}, series = {{European Journal of Heart Failure}}, title = {{Comparative diagnostic performance of machine learning models and traditional scores for HFpEF in older adults}}, url = {{http://dx.doi.org/10.1093/ejhf/xuag039}}, doi = {{10.1093/ejhf/xuag039}}, year = {{2026}}, }