Key result
An interpretable XGBoost machine learning model accurately predicted the risk of prevalent atrial fibrillation in middle-aged and older patients with coronary heart disease, achieving an AUC of 0.813 in the validation set.
Why the study?
Existing risk stratification tools inadequately capture the nonlinear, multidimensional determinants of AF in middle-aged and older CHD patients.
Does an interpretable machine learning model (XGBoost) accurately predict prevalent atrial fibrillation in middle-aged and older hospitalized patients with coronary heart disease?
Population
47,617 hospitalized CHD patients
Comparison
Eight machine learning algorithms for AF risk prediction
Design
Retrospective cohort study
Authors
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May aid AF risk stratification in CHD; hypothesis-generating and requires prospective validation before clinical use.
Cross-Sectional (n=47,617)
No
Does an interpretable machine learning model (XGBoost) accurately predict prevalent atrial fibrillation in middle-aged and older hospitalized patients with coronary heart disease?
Effect estimate: AUC 0.813 (95% CI 0.802-0.823)
p-value: p=<0.001
An interpretable XGBoost machine learning model using routine EMR data accurately identifies prevalent atrial fibrillation in middle-aged and older patients hospitalized with coronary heart disease.
Chen et al. (2026) conducted a cross-sectional in Coronary heart disease (CHD) (n=47,617). XGBoost machine learning model vs. Other machine learning models (GBDT, LightGBM, RF, AdaBoost, LR, DT, NB) was evaluated on Presence of atrial fibrillation (AF) during the index hospitalization (AUC 0.813, 95% CI 0.802-0.823, p=<0.001). An interpretable XGBoost machine learning model accurately predicted the risk of prevalent atrial fibrillation in middle-aged and older patients with coronary heart disease, achieving an AUC of 0.813 in the validation set.
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