Interpretable machine learning for chronic kidney disease prediction: Insights from SHAP and LIME analyses
Fig 2
Class distribution of the CKD datasets: (A) Dataset 1 before applying SMOTE, (B) Dataset 2 before applying SMOTE, (C) Dataset 1 after applying SMOTE within training folds, (D) Dataset 2 after applying SMOTE within training folds.
SMOTE was applied exclusively during the training phase of each cross-validation fold to prevent data leakage.