Mortality prediction of mitral valve replacement surgery by machine learning

Background: Mitral valve replacement procedure has increased in the Iran over the last years. For optimization of the results, as the other procedure, it needs statistical evaluation of the results, and then a system for the prediction of outcome. Hence, in this study, we generate a machine learning (ML)-based model to predict in-hospital mortality after isolated mitral valve replacement (IMVR). Materials and Methods: The patients who underwent IMVR from February 2005 to August 2016 were identified in a single tertiary heart hospital. Data were retrospectively gathered including baseline characteristics, echocardiographic and surgical features, and patient's outcome. Prediction models for in-hospital mortality were obtained using five supervised ML classifiers including: logistic regression (LR), linear discriminant analysis (LDA), support-vector machine (SVM), K-nearest neighbors (KNN), and multilayer perceptron (MLP). Results: A total of 1200 IMVRs were analyzed in our study. The study population was randomly divided into a training set (n = 840) and a testing set (n = 360). The overall in-hospital mortality was 4.2%. LR model had the best discrimination for 22 variables in predicting mortality after IMVR, with area under the receiver-operating curve (AUC), specificity, and sensitivity of 0.68, 0.73, and 0.58, respectively. A LDA model had an (AUC) of 0.73, compared to 0.56 for SVM, 0.51 for KNN, and 0.5 for MLP. Conclusions: We developed a robust ML-derived model to predict in-hospital mortality in patients undergoing IMVR. This model is promising for decision-making and deserves further clinical validation.