Simulation on GUSTO-I data shows class imbalance corrections fail to boost discrimination and impair calibration plus stability in clinical prediction models.
The Impact of Oversampling with SMOTE on the Performance of 3 Classifiers in Prediction of Type 2 Diabetes
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Class Imbalance Corrections Failed to Enhance Discrimination, Model Calibration, and Prediction Stability: An Empirical Simulation Study Based on Clinical Dataset
Simulation on GUSTO-I data shows class imbalance corrections fail to boost discrimination and impair calibration plus stability in clinical prediction models.