Simulation on GUSTO-I data shows class imbalance corrections fail to boost discrimination and impair calibration plus stability in clinical prediction models.
Understanding random resampling techniques for class imbalance correction and their consequences on calibration and discrimination of clinical risk prediction models
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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.