Advances in machine learning – moving cardiology to the next level
Dr. Lisa Tang and CCI Investigator Dr. Jason Andrade used machine learning with daily heart rhythm data to predict outcomes after ablation for atrial fibrillation (AF). Data were analysed from the CIRCA-DOSE randomised clinical trial that recorded daily measures, including heart rate variability and AF burden (total minutes in AF/day) from 343 patients referred for first catheter ablation due to AF refractory to at least one anti-arrhythmic drug. Comparative analyses were performed on 19 candidate model variants (e.g. stepwise regression, random forest, linear discriminant analysis etc.) to evaluate each model’s ability to identify patients predicted to experience at least one episode of AF recurrence post-ablation. They also examined the use of baseline comorbidities, echocardiographic parameters (e.g. left atrial volume) and CHA2DS2VASc scores. Multiple sets of threefold cross-validation experiments were conducted, each fold independently trained a candidate model with two thirds of the dataset and performed evaluation on the left-out samples.
