Does a random forest classifier improve the prediction of atrial fibrillation compared to the CHARGE-AF Cox model in patients with type 2 diabetes?
A machine learning random forest model matched the performance of the traditional CHARGE-AF model for predicting atrial fibrillation in patients with type 2 diabetes, highlighting distinct clinical and metabolic predictors.
Background: Machine learning (ML) may improve prediction of atrial fibrillation (AF), but its value compared with traditional models such as Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE-AF) in patients with diabetes remains unclear. Methods: Among 9,307 patients in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) with type 2 diabetes and no prior AF, a random forest (RF) classifier using clinical and metabolic variables was compared with a CHARGE-AF Cox model. Discrimination was assessed by five-fold cross-validated area under receiver operating curve (AUC). Results: = 0.18). Age, waist circumference, race, total cholesterol, and estimated glomerular filtration rate were the top predictors. Conclusion: ML matched CHARGE-AF performance and revealed distinct predictors supporting personalized AF risk prevention.
Offerman et al. (2026) studied this question.
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