Hi Theodora,
To calculate sensitivity and specificity for a trained neural network you can follow the below steps :-
- Ensure you have a test dataset with true labels and predictions from your neural network.
- Use the trained network to make predictions.
- Then identify the number of cases for True positive, True negative, False positive and False negative.
- Use below formulas to calculate sensitivity and specificity
sensitivity = TP / (TP + FN)
specificity = TN / (TN + FP)
y_true = [1, 0, 1, 1, 0, 1, 0, 0, 1, 0];
y_pred = [1, 0, 1, 0, 0, 1, 1, 0, 1, 0];
confMat = confusionmat(y_true, y_pred);
TP = confMat(2, 2);
TN = confMat(1, 1);
FP = confMat(1, 2);
FN = confMat(2, 1);
sensitivity = TP / (TP + FN);
specificity = TN / (TN + FP);
You may refer to the below MathWorks documentation to know more on “confusionmat” function:
