Sensitivity and Specificity

How do we know if a test or classifier is reliable? In both medicine and machine learning, classifier are used to attribute a class to an input data. Therefore, performance metrics are necessary to assess the reliability of the resulting classification. They help answer questions like:

• How often does the test correctly identify the true class?
• How often does the test mistakenly identify the wrong class?

On the Origin of Sensitivity and Specificity

Originally, sensitivity and specificity were qualities of a biological test used in chemistry, serology and immunology.

• Sensitivity measured how reactive a test was e.i. ability to detect minute quantities of the target antigen.
• Specificity measured how selective a test was e.i. ability to react exclusively with the target antigen and not with unrelated molecules.

Later, in clinical epidemiology, Jacob Yerushalmy in 1947 introduced sensitivity and specificity as probabilities to assess reliability of diagnoses in radiology.

• A measure of sensitivity or the probability of correct diagnosis of positive cases
• A measure of specificity or the probability of correct diagnosis of negative cases

Today, sensitivy and specificity are used as metrics beyond medecine, in data science and maching learning. They evaluate the performance of binary classifiers.

Binary Classifier

One can consider the medical diagnoses as binary classifier. A classifier is a process that takes input data and assigns it to a class. Such classifier is binary when it can only choose between two possible classes.

For instance, the diagnosis based on X-ray data only produce two possible outputs: Positive or Negative.

The Confusion Matrix

The confusion matrix is a table that compares classifier's prediction with the observed class.

• ✅ True Positive (TP): The classifier correctly predicted a positive outcome, the observed outcome was positive.
• ✅ True Negative (TN): The classifier correctly predicted a negative outcome, the observed outcome was negative.
• ❌ False Positive (FP): The classifier incorrectly predicted a positive outcome, the observed outcome was negative. It is also known as a Type I error.
• ❌ False Negative (FN): The classifier incorrectly predicted a negative outcome, the observed outcome was positive. It is also known as a Type II error.

Sensitivity

The sensitivity is the ratio of observed positive cases, correctly predicted by the test.

$$ \text{Sensitivity} = \frac{TP}{TP + FN} $$

Specificity

The specificity is the ratio of observed negative cases, correctly predicted by the test.

$$ \text{Specificity} = \frac{TN}{TN + FP} $$

Type I Error

The Type I Error or False Positive Rate impacts the precision of a test. It is the ratio of observed negative cases that the test failed to detect.

$$ \text{False Positive Rate} = \frac{FP}{FP + TN} $$

False Positive Rate can be expressed with specificity:

$$ \text{False Positive Rate} = {1 - Specificity} $$

Type II error

The Type II Error or False Negative Rate impacts the sensitivity of a test. It is the ratio of observed positive cases that the test failed to detect.

$$ \text{False Negative Rate} = \frac{FN}{FN + TP} $$

False Negative Rate can be expressed with sensitivity:

$$ \text{False Negative Rate} = {1 - Sensitivity} $$

Accuracy

The accuracy is how often the test correctly predicted the outcome for both positive and negative cases.

$$ \text{Accuracy} = \frac{TP + TN }{TN + TN + FP + FN} $$

Accuracy can be expressed as the weighted mean of sensitivity and specificity, weighted by the number of observed positive and negative cases.

$$ \text{Accuracy} = \frac{P ⋅ Sensitivity + N ⋅ Specificity }{P + N} $$

References

Statistical Problems in Assessing Methods of Medical Diagnosis, with Special Reference to X-Ray Techniques

Jacob Yerushalmy

Public health reports. 1947 October 03. DOI: 10.2307/4586294

On the Origin of Sensitivity and Specificity

Nicholas Binney, Christopher Hyde, and Patrick M. Bossuyt

Annals of Internal Medecine. 2021 March 16. DOI: 10.7326/M20-5028

ORION : a web server for protein fold recognition and structure prediction using evolutionary hybrid profiles

Yassine Ghouzam, Guillaume Postic, Pierre-Edouard Guerin, Alexandre G. de Brevern & Jean-Christophe Gelly

Scientific Reports. 2016 Jun 20. DOI: 10.1038/srep28268

Published on April 6, 2015

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• data-science

About the Author

Pierre-Edouard Guerin

Bioinformatician Research Scientist

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