Machine Learning as a Method 
ML is a tool that is increasing in popularity in data science as it identifies patterns in the data in order to predict outcomes. The use of ML in medicine is rapidly expanding with the development of large, scale, digitized medical data that could be used with ML for clinical prediction. ML is a well-suited option for this STS-based study for many reasons. First, ML is considered ”data-driven knowledge discovery” and as such, no prior knowledge is needed of a predictor’s weight. The STS database contains numerous low-granularity predictors that have unclear significance. Using ML allowed the authors to use any variable that exists within the database knowing that modelling will be able to exclude those that are not significant for prediction. While, with traditional statistical methods, the addition of numerous predictors introduces collinearity, this is managed by ML as modelling is not based on linear relationships. As such, in this study, 53 predictors of unknown significance were used with 3 ML models to predict recurrent MR and death with great success.