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In medicine and statistics, sensitivity and specificity mathematically describe the accuracy of a test that reports the presence or absence of a medical condition. If individuals who have the condition are considered "positive" and those who do not are considered "negative", then sensitivity is a measure of how well a test can identify true ...
Example of receiver operating characteristic (ROC) curve highlighting the area under the curve (AUC) sub-area with low sensitivity and low specificity in red and the sub-area with high or sufficient sensitivity and specificity in green.
The relationship between sensitivity and specificity, as well as the performance of the classifier, can be visualized and studied using the Receiver Operating Characteristic (ROC) curve. In theory, sensitivity and specificity are independent in the sense that it is possible to achieve 100% in both (such as in the red/blue ball example given above).
They use the sensitivity and specificity of the test to determine whether a test result usefully changes the probability that a condition (such as a disease state) exists. The first description of the use of likelihood ratios for decision rules was made at a symposium on information theory in 1954. [ 1 ]
The log diagnostic odds ratio can also be used to study the trade-off between sensitivity and specificity [5] [6] by expressing the log diagnostic odds ratio in terms of the logit of the true positive rate (sensitivity) and false positive rate (1 − specificity), and by additionally constructing a measure, :
Specificity will generally be higher than sensitivity, especially when people have COVID-19 symptoms—in other words, false-negative COVID-19 tests are more likely than false positives.
For example, a receiver sensitivity of −98 dBm is better than a receive sensitivity of −95 dBm by 3 dB, or a factor of two. In other words, at a specified data rate, a receiver with a −98 dBm sensitivity can hear signals that are half the power of those heard by a receiver with a −95 dBm receiver sensitivity. [citation needed].
Youden's J statistic is = + = + with the two right-hand quantities being sensitivity and specificity.Thus the expanded formula is: = + + + The index was suggested by W. J. Youden in 1950 [1] as a way of summarising the performance of a diagnostic test; however, the formula was earlier published in Science by C. S. Pierce in 1884. [2]