Tool for statistical calculation of Sensitivity and Specificity. Sensitivity and Specificity are two criteria of statistical measures evaluating probabilities of presence or absence of phenomena.

Sensitivity and Specificity - dCode

Tag(s) : Data Processing

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Statistically, **sensitivity** is the ratio of the number of true positives to the total number of positives (including those declared false by mistake).

$$ \text {Sensitivity} = \frac {\text {True positives}} {\text {True positives} + \text {False negative}} $$

__Example:__ A set of individuals: `A,B,C,D,E` (5 elements, all sick), with as declared as sick `A,B,C` (3 elements) and 'D,E 'declared not sick by mistake. Sensitivity is $$ S = \frac{3}{5} = 60 \% $$

**Specificity** is the ratio of the number of true negatives to the total number of negative elements (including those declared true by mistake).

$$ \text {Specificity} = \frac {\text {True negatives}} {\text {True negatives} + \text {False positives} $$

__Example:__ A set of of individuals: `A,B,C,D,E` (5 elements, all healthy), with as declared healthy `A,B` (2 elements) and `C,D,E` declared sick by mistake. Specificity is $$ S = \frac{2}{5} = 40 \% $$

The most common use of **sensitivity and specificity** is for diagnostic tests of disease (which have an error rate), it is then possible to determine their quality: the more sensibility and specificity are strong (close to 1 or 100%), more they are reliable.

The calculation of predictive value involves both **sensitivity and specificity** values.

In computer science, prefer to use the values of precision and recall.

For the calculation of other values, see the confusion matrix.

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