Machine Learning Methods for Prediction of Hospital Mortality in Patients with Coronary Heart Disease after Coronary Artery Bypass Grafting

Aim To compare the accuracy of predicting an in-hospital fatal outcome for models based on current machine-learning technologies in patients with ischemic heart disease (IHD) after coronary bypass (CB) surgery. Material and methods A retrospective analysis of 866 electronic medical records was performed for patients (685 men and 181 women) who have had a CB surgery for IHD in 2008–2018. Results of clinical, laboratory, and instrumental evaluations obtained prior to the CB surgery were analyzed. Patients were divided into two groups: group 1 included 35 (4 %) patients who died within the first 20 days of CB, and group 2 consisted of 831 (96 %) patients with a beneficial outcome of the surgery. Predictors of the in-hospital fatal outcome were identified by a multistep selection procedure with analysis of statistical hypotheses and calculation of weight coefficients. For construction of models and verification of predictors, machine-learning methods were used, including the multifactorial logistic regression (LR), random forest (RF), and artificial neural networks (ANN). Model accuracy was evaluated by three metrics: area under the ROC curve (AUC), sensitivity, and specificity. Cross validation of the models was performed on test samples, and the control validation was performed on a cohort of patients with IHD after CB, whose data were not used in development of the models. Results The following 7 risk factors for in-hospital fatal outcome with the greatest predictive potential were isolated from the EuroSCORE II scale: ejection fraction (EF)