Speaker
Description
Classification plays a pivotal role in medicine for both diagnostic and prognostic purposes. Traditionally, diagnostic efficacy is evaluated using prevalence-independent metrics, such as sensitivity and specificity. For numerical tests, the Area Under the Receiver Operating Characteristic (ROC) curve is the standard for assessing classification success. However, the rising adoption of machine learning in medicine has popularized metrics like precision (positive predictive value), recall (sensitivity), and the F-measure. While Precision-Recall (PR) curves are increasingly used to evaluate binary classification, it is well established that precision is heavily influenced by disease prevalence. Consequently, using standard PR curves without accounting for prevalence can introduce bias into performance assessments. In this study, we propose prevalence-corrected PR curves as a robust alternative to eliminate this bias. Through simulation scenarios designed to reflect real-world medical contexts, we demonstrate that the proposed method provides a more unbiased evaluation of classification performance compared to standard PR curves.
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