Salīdzināt metodes

	ROC analīze (Receiver Operating Characteristic)	Jutīgums un specifiskums
Nozare	Statistika	Pētniecības statistika
Saime	Hypothesis test	Process / pipeline
Izcelsmes gads	1954 (signal detection); 1982 (AUC formalization)	1978
Autors	Peterson, Birdsall & Fox (signal detection theory); Hanley & McNeil (medical statistics)	Multiple sources in medical diagnosis and signal detection
Tips	Diagnostic accuracy evaluation	Concept
Pirmavots	Hanley, J. A., & McNeil, B. J. (1982). The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143(1), 29–36.	Altman, D. G., & Bland, J. M. (1994). Diagnostic tests 1: Sensitivity and specificity. BMJ, 308(6943), 1552.
Citi nosaukumi	ROC curve analysis, AUC analysis, sensitivity-specificity analysis, diagnostic accuracy analysis	diagnostic accuracy, true positive rate, true negative rate, receiver operating characteristic
Saistītās	4	4
Kopsavilkums	ROC analysis evaluates how well a continuous or ordinal test variable discriminates between two binary outcome classes. By plotting the true positive rate (sensitivity) against the false positive rate (1 − specificity) across all decision thresholds, it produces a curve whose area under the curve (AUC) quantifies overall discriminative power, ranging from 0.5 (chance) to 1.0 (perfect discrimination).	Sensitivity and specificity are fundamental metrics of diagnostic test accuracy. Sensitivity is the probability that a test correctly identifies a person with the disease (true positive rate: TP / (TP + FN)). Specificity is the probability that a test correctly identifies a person without the disease (true negative rate: TN / (TN + FP)). Every test involves a trade-off: increasing sensitivity (catching all sick people) often reduces specificity (more false alarms). Choice of test threshold depends on the clinical context: screening for serious diseases favors sensitivity; confirming a diagnosis favors specificity.