เปรียบเทียบวิธี

	การวิเคราะห์ ROC (Receiver Operating Characteristic)	ความไวและความจำเพาะ
สาขาวิชา	สถิติศาสตร์	สถิติการวิจัย
ตระกูล	Hypothesis test	Process / pipeline
ปีกำเนิด	1954 (signal detection); 1982 (AUC formalization)	1978
ผู้ริเริ่ม	Peterson, Birdsall & Fox (signal detection theory); Hanley & McNeil (medical statistics)	Multiple sources in medical diagnosis and signal detection
ประเภท	Diagnostic accuracy evaluation	Concept
แหล่งต้นตำรับ	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.
ชื่อเรียกอื่น	ROC curve analysis, AUC analysis, sensitivity-specificity analysis, diagnostic accuracy analysis	diagnostic accuracy, true positive rate, true negative rate, receiver operating characteristic
ที่เกี่ยวข้อง	4	4
สรุป	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.