Diagnostic Odds Ratio Calculator
Calculate the diagnostic odds ratio to summarize a classifier's discriminative power.
About Diagnostic Odds Ratio Calculator
The Diagnostic Odds Ratio (DOR) is a single-number summary of how well a classifier discriminates between positive and negative cases: DOR = (TP × TN) / (FP × FN). It expresses the odds of a positive prediction occurring in someone who truly has the condition, relative to the odds of a positive prediction in someone who doesn't. A DOR of 1 means the test has no discriminative value; higher values indicate better discrimination, and DOR is undefined (infinite) when there are zero false positives or false negatives.
Formula
DOR = (TP * TN) / (FP * FN)
- TP
- True positives
- TN
- True negatives
- FP
- False positives
- FN
- False negatives
Worked example
With the default values below, Diagnostic Odds Ratio Calculator returns Diagnostic Odds Ratio of 340.0000. Change any input above and every figure here is recalculated in your browser.
Inputs used
- True Positives (TP)accepts 0–1,000,000,000
- 80
- True Negatives (TN)accepts 0–1,000,000,000
- 850
- False Positives (FP)accepts 0–1,000,000,000
- 20
- False Negatives (FN)accepts 0–1,000,000,000
- 10
Results
- Diagnostic Odds Ratio
- 340.0000
- TP × TN
- 68,000
- FP × FN
- 200
Step by step
- 1Numerator: TP × TN= 68000
- 2Denominator: FP × FN= 200
- 3DOR: numerator / denominator= 340.0000
Assumptions and limits
- The result is derived only from the 4 inputs listed above. Anything not among them is not part of the calculation.
- In particular, this calculator does not take inflation, tax, fees or charges or compounding frequency into account.
- Figures are rounded for display to 4 decimal places, so totals built from several rounded parts can differ slightly from the unrounded arithmetic.
How these calculators are built and tested is described on the Methodology page. If a figure looks wrong, please report it.
Frequently Asked Questions
What does a DOR of 1 mean?
A DOR of 1 means the odds of a positive test result are the same whether or not the condition is truly present, indicating the test provides no discriminative information at all.
Why can DOR be undefined?
If either FP or FN is zero, the denominator becomes zero and DOR is mathematically undefined (infinite) — this happens with a 'perfect' classifier on the given data, and in practice a small correction (like adding 0.5 to each cell) is sometimes applied to avoid this.
How does DOR compare to sensitivity and specificity?
DOR combines both sensitivity and specificity information into one number via the odds ratio, which is convenient for ranking classifiers, but it can obscure whether performance is driven by the positive or negative class, so it's best used alongside — not instead of — sensitivity and specificity.