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Table 11 Survival prediction results including the follow-up time – 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
Logistic regressionblue+0.616*blue0.719*blue0.838*blue0.785*blue0.860*blue0.617*blue0.822*
(EF, SR, & FU)       
Logistic regression+0.6070.7140.8330.7800.8560.6120.818
(all features)       
  1. Top row: logistic regression using only ejection fraction (EF), serum creatinine (SC), and follow-up time month (FU). Bottom row: logistic regression using all features. 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). We reported bluein blue and with the top results for each score.