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.