Advancements in machine learning (ML) have transformed the landscape of heart failure prediction and management (HF) in recent years. Traditional clinical risk models, such as the MAGGIC and Seattle Heart Failure models, have been enhanced by ML approaches that leverage high-dimensional patient data to improve predictive accuracy. Among these, ensemble models combining multiple algorithms, including Random Forest, Gaussian Naive Bayes, Decision Tree, and k-Nearest Neighbor (kNN), have shown superior performance in predicting HF outcomes [15,16]. A recent meta-analysis reported that the ensemble models achieved an average AUC-ROC of 0.85 in multiple HF datasets, outperforming individual models and highlighting their robustness in diverse clinical contexts [17].

Deep learning models have further pushed the boundaries of HF prediction, with sequence-based architectures proving particularly effective in capturing complex temporal dependencies in longitudinal patient data. Transformer-based models, such as TRisk, have demonstrated exceptional performance in predicting 36-month mortality in patients with HF by analyzing large-scale electronic health records (EHRs), achieving a concordance index of 0.845 and outperforming traditional models such as MAGGIC-EHR [18]. Similarly, Long-Short-Term Memory (LSTM) networks have shown promise in time series forecasting tasks, where their ability to capture long-term dependencies has led to improvements in predictive performance and robustness, particularly in multicenter datasets [19, 20].

Despite impressive predictive performance, the clinical utility of these models remains a critical area of investigation. Decision curve analysis (DCA) has emerged as a robust method to assess the net benefit of predictive models across varying probability thresholds, enabling an evaluation of their clinical relevance beyond standard performance metrics. Recent studies have highlighted that models with high AUC-ROC scores do not always produce superior clinical utility, emphasizing the need for threshold optimization to ensure that predictive gains translate into meaningful improvements in clinical outcomes [21, 22]. In particular, a comparative analysis of ML models applied to the prediction of HF demonstrated that DCA provided crucial insights into the trade-off between sensitivity and specificity at different probability thresholds, strengthening the importance of contextualizing the performance of the model within a clinical decision-making framework [11].

Fairness and bias mitigation remain central concerns in the implementation of ML models for HF prediction, particularly with respect to gender disparities. Several recent investigations have discovered complementary gender biases in different ML algorithms, where specific models exhibited higher predictive performance for female patients, while others disproportionately favored male patients [23, 24]. For example, an earlier comparison between the XGBoost and SVM models identified opposing bias directions, where XGBoost favored males, but SVM favored the females when performance was considered. These discrepancies make a case for ensemble approaches, which help counter algorithmic bias through the combination of strengths of different models to be fair in the clinical use of AI in healthcare delivery. Biases like this can also be countered by applying demographic-specific threshold tuning and recalibration, as previously suggested, which would balance the model performance in different patient subgroups [25]

Interpretability remains a key concern in the clinical use of ML models because transparent models are needed for clinicians to establish trust with patients. Shapley Additive exPlanations (SHAP) analysis has become widely used to interpret the importance of features and explain the predictions of the ML model in clinical applications. For example, a recent study that employs SHAP analysis to interpret a predictive model for 1-year all-cause readmission in elderly patients with HF identified hemoglobin levels, NT-proBNP, smoking history, NYHA classification and left ventricular ejection fraction as the main contributors to risk prediction [13]. These findings align with typical clinical practice, validating model prediction and enhancing the models’ clinical utility. SHAP analysis has also been useful in identifying characteristic interactions varying between subgroups of patients, allowing a more targeted and personal risk assessment [26].

In spite of these developments, there are still challenges to making predictive models generalize well to diverse demographics and clinical environments. When applied to new environments, variations in clinical protocols, data quality, and demographic characteristics between institutions can result in unwanted biases and deterioration in model performance [14]. Stringent external validation, domain adaptation strategies, and ongoing model recalibration are needed to address these concerns and preserve the models’ clinical utility. The effective and equitable implementation of ML models for the prediction and management of HF will depend on the integration of interpretability frameworks, fairness audits, and clinical impact assessments as the field evolves.