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Table 9 Survival prediction results on serum creatinine and ejection fraction – mean of 100 executions

From: Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone

MethodMCCF1 scoreAccuracyTP rateTN ratePR AUCROC AUC
Random forestsblue+0.418*blue0.754*blue0.585*0.541blue0.855*0.5410.698
Gradient boosting+0.4140.750blue0.585*blue0.550*0.845blue0.673*blue0.792*
SVM radial+0.3480.7200.5430.5190.8160.4940.667
  1. MCC: Matthews correlation coefficient. TP rate: true positive rate (sensitivity, recall). TN rate: true negative rate (specificify). Confusion matrix threshold for MCC, F1 score, accuracy, TP rate, TN rate: τ=0.5. PR: precision-recall curve. ROC: receiver operating characteristic curve. AUC: area under the curve. MCC: worst value = –1 and best value = +1. F1 score, accuracy, TP rate, TN rate, PR AUC, ROC AUC: worst value = 0 and best value = 1. MCC, F1 score, accuracy, TP rate, TN rate, PR AUC, ROC AUC formulas: Additional file 1 (“Binary statistical rates” section). Gradient boosting: eXtreme Gradient Boosting (XGBoost). SVM radial: Support Vector Machine with radial Gaussian kernel. We reported bluein blue and with the top results for each score.