Zhang et al. study the use of machine learning models to determine whether preoperative intra-aortic balloon pump (IABP) implantation would be used in patients undergoing coronary artery bypass grafting (CABG). Patients undergoing CABG were divided into preoperative and non-preoperative IABP implantation groups. Machine learning models were developed through the LassoCV algorithm and their performance was analyzed through AUC values and kolmogrov-smirnov plots. The Gaussian Naïve Bayes model showed the most accurate prediction ability, with an AUC of 0.76 in the training set, 0.72 in the validation set, and a good KS plot. The model also provided analyzable predictions through the SHapley Additive exPlantations force analysis, which highlighted the most influential risk factors in the implantation. The researchers concluded that the Gaussian Naïve Bayes model outperformed other machine learning models, thus supporting its promising potential to aid in preoperative decision-making for patients undergoing CABG.