Class imbalance correction in artificial intelligence models leads to miscalibrated clinical predictions: a real-world evaluation

2026-03-05 health informatics Title + abstract only

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BackgroundPredictive models employing machine learning algorithms are increasingly being used in clinical decision making, and improperly calibrated models can result in systematic harm. We sought to investigate the impact of class imbalance correction, a commonly applied preprocessing step in machine learning model development, on calibration and modelled clinical decision making in a large real-world context. MethodsA histogram boosted gradient classifier was trained on a highly imbalanced na...

Class imbalance correction in artificial intelligence models leads to miscalibrated clinical predictions: a real-world evaluation

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