Abstract:
Diagnostic testing refers to the classification of the presence or absence of specific conditions using an index variable. Commonly utilized tools in this domain include the Receiver Operating Characteristic curves and their Area Under the Curve. These methodologies assess the ability of various thresholds of the index variable to classify a binary outcome correctly. However, when applied to imbalanced data, Area Under the Curve. can provide an overly optimistic assessment and may evaluate thresholds that lack practical relevance in real-world scenarios. To address these limitations, our study introduces the area under the Critical Success Index curve as a novel metric for evaluating diagnostic performance. The area under the Critical Success Index curve prioritizes the most relevant thresholds, making it less dependent on Prevalence rates. We demonstrate that the area under the Critical Success Index curve provides a more consistent measure of diagnostic ability when Prevalence is low, favoring those thresholds with less Quantity disagreement.