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Fig. 2 | BMC Medical Informatics and Decision Making

Fig. 2

From: Second opinion machine learning for fast-track pathway assignment in hip and knee replacement surgery: the use of patient-reported outcome measures

Fig. 2

PPV-Sensitivity Curves comparing the performance of all developed models. This graph provides a visual comparison of the PPV-Sensitivity curves for each classifier developed in our analysis: Hierarchical Shrinked Trees (HST), Fast Interpretable Greedy Sums (FIGS), Decision Trees (DT), Random Forest (RF), K-Nearest Neighbors (KNN), Logistic Regression (LR), eXtreme Gradient Boosting (XGB), Support Vector Machines (SVM) and Multi-layer Perceptron (MLP). Each line traces the trade-off between sensitivity (true positive rate) and PPV (positive predictive false) across different thresholds. The Area Under the Precision-Recall (or Positive Predictive Value and Sensitivity) Curve (AUPRC) is provided for each as a measure of overall performance

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BMC Medical Informatics and Decision Making

ISSN: 1472-6947

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