Sensitivity Calculator
Calculate test sensitivity, specificity, and diagnostic performance metrics
Calculate Test Sensitivity
Sick people correctly identified as positive
Sick people incorrectly identified as negative
Sensitivity Results
Formula: Sensitivity = TP / (TP + FN)
Calculation: 0 / (0 + 0) = 0.00%
Sensitivity Interpretation Guide
High Sensitivity (≥85%)
- • Excellent at detecting positive cases
- • Few false negatives
- • Good for screening tests
- • Negative result is reassuring
Low Sensitivity (<70%)
- • Misses many positive cases
- • Many false negatives
- • Not ideal for screening
- • Negative result less reliable
Quick Reference
Sensitivity
True Positive Rate
TP / (TP + FN)
Specificity
True Negative Rate
TN / (TN + FP)
PPV
Positive Predictive Value
TP / (TP + FP)
NPV
Negative Predictive Value
TN / (TN + FN)
Clinical Applications
Medical diagnostic tests evaluation
Screening program effectiveness
Quality control in laboratories
Binary classification model evaluation
Public health surveillance
Understanding Sensitivity in Diagnostic Testing
What is Sensitivity?
Sensitivity, also known as the true positive rate, measures a test's ability to correctly identify positive cases. It answers the question: "Of all people who have the condition, what percentage does the test correctly identify?"
Clinical Interpretation
- •High sensitivity: Excellent for ruling out disease when negative
- •Low sensitivity: Many cases will be missed (false negatives)
- •Ideal for screening tests to catch all possible cases
Mathematical Foundation
Sensitivity = TP / (TP + FN)
= True Positives / Total with Condition
Key Relationships
- TP: True Positives (correctly identified as positive)
- FN: False Negatives (missed positive cases)
- TP + FN: Total number with the condition
- Range: 0% (no detection) to 100% (perfect detection)
Remember: High sensitivity minimizes false negatives but may increase false positives!
Examples and Applications
🩺 Medical Screening
Example: COVID-19 rapid test
TP: 95 infected people test positive
FN: 5 infected people test negative
Sensitivity: 95/100 = 95%
Interpretation: Excellent for screening
🔬 Laboratory Test
Example: Cancer biomarker
TP: 70 cancer patients test positive
FN: 30 cancer patients test negative
Sensitivity: 70/100 = 70%
Interpretation: Moderate; needs improvement
🤖 Machine Learning
Example: Fraud detection model
TP: 85 fraudulent transactions detected
FN: 15 fraudulent transactions missed
Sensitivity: 85/100 = 85%
Interpretation: Good but can be optimized