Analysis of Diagnostic Accuracy Measures

Initializing live version
Download to Desktop

Requires a Wolfram Notebook System

Interact on desktop, mobile and cloud with the free Wolfram Player or other Wolfram Language products.

This Demonstration shows plots of various accuracy measures for diagnostic tests on normally distributed nondiseased and diseased populations. This is done for differing prevalence of the disease, taking into account mean and standard deviations of the populations. The mean and standard deviations are expressed in arbitrary units. The measures considered are the positive prognostic value ("PPV"), the negative prognostic value ("NPV"), the (diagnostic) odds ratio ("OR"), the likelihood ratio for a positive result ("LR+"), and the likelihood ratio for a negative result ("LR-"). The measures are plotted against the sensitivity or the specificity of the test, which can be selected by clicking the respective button.

Contributed by: Theodora Chatzimichail (July 2015)
(Hellenic Complex Systems Laboratory)
Based on the Demonstration Uncertainty of Measurement and Diagnostic Accuracy Measures by: Aristides T. Hatjimihail
Open content licensed under CC BY-NC-SA


The measures that are used in the evaluation of the clinical accuracy of a diagnostic test applied to a diseased or nondiseased population can be calculated as functions of the sensitivity or the specificity of the test. Sensitivity is the fraction of the diseased population with a positive test result, while specificity is the fraction of the nondiseased population with a negative test result. If we denote by the sensitivity, the specificity, and the prevalence, we have:

, , , , .

In the thumbnail and the snapshots, the population data describes a bimodal distribution of serum glucose measurements in nondiabetic and diabetic populations [2].

This Demonstration is a simplified version of another Demonstration [1], and is appropriate as an educational tool for medical students.


[1] A. T. Hatjimihail, "Uncertainty of Measurement and Diagnostic Accuracy Measures", Wolfram Demonstrations Project, 2009.

[2] T. O. Lim, R. Bakri, Z. Morad, and M. A. Hamid, "Bimodality in Blood Glucose Distribution: Is It Universal?", Diabetes Care, 25(12), 2002 pp. 2212–2217.


Feedback (field required)
Email (field required) Name
Occupation Organization
Note: Your message & contact information may be shared with the author of any specific Demonstration for which you give feedback.