Fig. 3 | Scientific Reports

Fig. 3

From: Interpretable machine-learning risk prediction of unplanned extubation among cancer patients with peripherally inserted central catheters

Fig. 3

Optimal multiple analysis based on machine-learning algorithm. Note: (A) Comparison of three mainstream variable encoding methods; (B) Comparison of four methods for data imbalance processing; (C) Comparison of four ML models; (D) Validation of the advantages and disadvantages of the four XGB models; (E) Decision curve analysis of three mainstream variable encoding methods; (F) Decision curve analysis of four methods for data imbalance processing; (G) Decision curve analysis of four ML models; (H) Decision curve analysis of the four XGB models. WoE, weight of encoding; CE, counting encoder; ENN, edited nearest neighbor; SMOTE, synthetic minority over-sampling technique; XGB, extreme gradient boosting; SVM, support vector machine; FC, feature construction; FL, focal loss.

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