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

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

Method	MCC	F₁ score	Accuracy	TP rate	TN rate	PR AUC	ROC AUC
Random forests	blue+0.418*	blue0.754*	blue0.585*	0.541	blue0.855*	0.541	0.698
Gradient boosting	+0.414	0.750	blue0.585*	blue0.550*	0.845	blue0.673*	blue0.792*
SVM radial	+0.348	0.720	0.543	0.519	0.816	0.494	0.667

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.

ISSN: 1472-6947