Commonly used evaluation measures such as Recall, Precision, and F-Measure are biased by class prevalence and label bias, whereas Informedness and Markedness provide unbiased estimates of performance.
AUROC alone is insufficient to evaluate prediction models in imbalanced health care scenarios; routine reporting of class-aware metrics is essential for clinically meaningful evaluation.
Abstract Background Predictive models increasingly support clinical decision-making, although imbalanced outcome distributions are common in health care datasets and can distort performance evaluation. The area under the receiver operating characteristic curve (AUROC) remains the most frequently reported metric, despite its limited ability to reflect clinically meaningful performance under class imbalance. Objective This study aimed to examine the influences of metric selection on the clinical interpretation of predictive models in imbalanced real-world health care data. Methods This was a retrospective cohort study, including 17,018 hospitalized patients with COVID-19. Two predictive models using extreme gradient boosting (XGBoost) were developed to predict kidney replacement therapy (KRT) and mortality. Model performance was assessed using AUROC, macro- F 1 -score, class-specific precision and recall, calibration (curve, slope, and intercept), decision curve analysis, and learning curves. Standard rebalancing strategies were applied exclusively to the training data to evaluate their impact on performance. Results KRT occurred in 9.5%, and mortality in 18.0%. Although AUROC values were high (0.928 for KRT and 0.945 for mortality), performance in the minority class was substantially lower. For KRT, precision was 0.539 and recall 0.372; for mortality, precision was 0.725 and recall 0.718. Rebalancing strategies were associated with higher recall for the minority class, but this gain was accompanied by a reduction in precision, with minimal impact on AUROC values. As a result, AUROC remained high despite clinically relevant changes in error distribution between false positives and false negatives. The learning curves show a plateau-like shape, with stable validation performance across all training set sizes for both outcomes. Conclusions AUROC alone is insufficient to evaluate prediction models in imbalanced health care scenarios, even with rebalancing. Routine reporting of class-aware metrics, alongside learning curve analysis, is essential to support robust and clinically meaningful evaluation of predictive models, rather than their direct translation into practice.
Ventura et al. (Wed,) reported a other. Commonly used evaluation measures such as Recall, Precision, and F-Measure are biased by class prevalence and label bias, whereas Informedness and Markedness provide unbiased estimates of performance.