Diagnostic Accuracy
The Diagnostic Accuracy calculator evaluates how well a diagnostic test identifies a condition, given the four cells of a confusion matrix.
Step 1 — Enter the confusion matrix
| Cell | Meaning |
|---|---|
| True Positives (TP) | Test positive, disease present |
| False Positives (FP) | Test positive, disease absent |
| False Negatives (FN) | Test negative, disease present |
| True Negatives (TN) | Test negative, disease absent |
Also supports Excel import and Sample Data Generator.
Step 2 — Calculate
Click Calculate Diagnostic Accuracy.
Step 3 — Read the results
All metrics with 95% CI:
- Sensitivity — TP / (TP + FN)
- Specificity — TN / (TN + FP)
- Positive Predictive Value (PPV) — TP / (TP + FP)
- Negative Predictive Value (NPV) — TN / (TN + FN)
- Positive Likelihood Ratio (LR+) — Sensitivity / (1 - Specificity)
- Negative Likelihood Ratio (LR-) — (1 - Sensitivity) / Specificity
- Accuracy — (TP + TN) / N
- Prevalence — (TP + FN) / N
- Youden's Index — Sensitivity + Specificity - 1
- Diagnostic Odds Ratio (DOR) — (TP * TN) / (FP * FN)
Additional panels:
- Predictive Value Graphs — PPV/NPV vs prevalence
- Fagan's Nomogram — pre-test to post-test probability
- Custom Prevalence Input — Bayes-theorem based PPV/NPV at any prevalence
- Materials and Methods — auto-generated text for manuscripts
Step 4 — Multiple analyses history
Every calculation is logged in an Analysis Summary Table. Export all to Excel or clear history.
A UniversalChatBot is available below.
Statistical methods used
Confidence intervals — Wilson Score Method
All proportion-based metrics use Wilson Score CI:
denominator = 1 + z^2/n
centre = (p + z^2/(2n)) / denominator
margin = z * sqrt(p(1-p)/n + z^2/(4n^2)) / denominator
Likelihood ratio CIs — computed on log scale and back-transformed.
Custom prevalence (Bayes theorem)
PPV = (Se * prev) / (Se * prev + (1-Sp) * (1-prev))
NPV = (Sp * (1-prev)) / ((1-Se) * prev + Sp * (1-prev))
Clinical utility tiers
| LR+ | LR- | Label |
|---|---|---|
| > 10 | < 0.1 | Excellent |
| > 5 | < 0.2 | Good |
| > 2 | < 0.5 | Fair |
| Otherwise | Limited |