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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