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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

Method

MCC

F1 score

Accuracy

TP rate

TN rate

PR AUC

ROC AUC

Logistic regression

blue+0.616*

blue0.719*

blue0.838*

blue0.785*

blue0.860*

blue0.617*

blue0.822*

(EF, SR, & FU)

       

Logistic regression

+0.607

0.714

0.833

0.780

0.856

0.612

0.818

(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.
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BMC Medical Informatics and Decision Making

ISSN: 1472-6947

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