Key result
CatBoost ML model integrating ECG, phonocardiogram, and clinical features detects heart failure with ~1.00 AUC.
Why the study?
Heart failure is a major health concern with poor prognosis, creating an urgent clinical need for an easy and accurate screening method.
Does a machine learning model incorporating integrated ECG and PCG features accurately screen for heart failure in adult patients?
Cohort (n=1,017)
Independent reviewers blinded to feature analyses and model predictions
Yes
Does a machine learning model incorporating integrated ECG and PCG features accurately screen for heart failure in adult patients?
A machine learning model integrating ECG and PCG features with clinical variables demonstrated excellent diagnostic performance for early heart failure screening.
Answers are grounded in this paper and cite their sources.
May aid noninvasive HF screening; hypothesis-generating and should not yet change practice.
Backgrounds: Heart failure (HF) is a major health concern associated with poor prognosis, and there is an urgent clinical need for an easy and accurate method for screening HF. This multicenter study aims to validate a novel AI-based phono-electrocardiogram algorithm (AI-PECG) in early HF detection. Methods: A total of 1,017 individuals were grouped into a training cohort and an external validating cohort, with a ratio of 8:2. In the training cohort, data of patients were further split into training set and test set randomly with the 8:2 ratio. The least absolute shrinkage and selection operator with five-fold cross-validation was utilized for dimensionality reduction and selection of features for model construction from clinical variables, phonocardiogram (PCG) parameters and electrocardiogram (ECG) parameters. Five machine learning (ML) algorithms were then carried out to choose a classifier model with the optimal recognition of HF, including logistic regression, random forest, eXtreme Gradient Boosting, Category Boosting (CatBoost), and Naive Bayes. The importance of ranking predicted factors was calculated in the final screening model using the SHapley Additive exPlanations analysis. Results: Among eligible participants, 302 reported HF. Totally 17 variables were selected to conduct the screening models. In the training set, the area under the curve (AUC) of the CatBoost model was 0.998 [95% confidence interval (CI): 0.996-1.000], which was higher compared to that of other ML models. The sensitivity and specificity of CatBoost model was 0.989 (95% CI: 0.978-0.996) and 0.989 (95% CI: 0.979-0.999). In the screening model, top 5 factors in terms of importance were EMAT, lymphocyte, LVST, CRP, and platelet. Conclusion: The ML model incorporating general data alongside ECG and PCG features carried out good detection performance for HF. This had the potential to be an available tool for clinicians to screen HF patients as early as possible for further clinical interventions.
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Chee et al. (2025) conducted a cohort in Heart failure (n=1,017). CatBoost machine learning model integrating ECG, PCG, and clinical features was evaluated on Area under the curve (AUC) for heart failure detection (CatBoost model, training set) (AUC 0.998, 95% CI 0.996-1.000). The CatBoost machine learning model integrating electrocardiogram, phonocardiogram, and clinical features accurately detected heart failure with an area under the curve of 0.998.
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