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Interpretable machine learning for chronic kidney disease prediction: Insights from SHAP and LIME analyses

Fig 4

ROC curve comparison for Dataset 2 (UCI CKD).

Panel A: without SMOTE; Panel B: with SMOTE. XGBoost achieves optimal performance (AUC = 0.948 ± 0.013) with SMOTE.

Fig 4

doi: https://doi.org/10.1371/journal.pone.0343205.g004