Methods
A retrospective cohort study was conducted in patients with PPM discharged from a large academic health center in New York City from 2006 through 2016. Risk factors identified through bivariate analysis were used to build predictive models. Five-fold cross-validation was applied to build models, then the performance of the three machine learning models–logistic regression, decision tree (DT), and support vector machine (SVM)– for predicting surgical site infection (SSI) in patients with a permanent pacemaker (PPM) was compared.