Receiver Operating Characteristic (ROC) Curve Analysis for Medical Diagnostic Test Evaluation.

By Karimollah Hajian-Tilaki. Caspian Journal of Internal Medicine, 2013

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A survey on missing data in machine learning (2021, background): “The AUC like other performance measures also has its advantages, it allows for a visualised graphical representation of imputation performance and is also unaffected by abnormal distributions in the population and decision criterion [151].”
Elevated Calprotectin and Abnormal Myeloid Cell Subsets Discriminate Severe from Mild COVID-19 (2020, unknown): “To measure the global performance of this test, we used a receiver operating characteristic (ROC) curve (Hajian-Tilaki, 2013).”
Receiver operating characteristic curve analysis in diagnostic accuracy studies: A guide to interpreting the area under the curve value (2023, background): “[9] ] The ROC curve plots TPF (sensitivity) and FPF (1 – specificity) values for each index test outcome on an x‑y coordinate graph.”
Receiver operating characteristic curve: overview and practical use for clinicians (2022, background): “lence, meaning that samples can be taken regardless of the prevalence of a disease in the population [9].”