APPLICATION OF SUPERVISED LEARNING
DEEP LEARNING
Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]


Specificity


Sensitivity


Accuracy


None of the above

Detailed explanation1: Sensitivity is the proportion of true positives that are correctly predicted by the model, while specificity is the proportion of true negatives that are correctly predicted by the model.
Detailed explanation2: The more sensitive a test, the less likely an individual with a negative test will have the disease and thus the greater the negative predictive value. The more specific the test, the less likely an individual with a positive test will be free from disease and the greater the positive predictive value.
Detailed explanation3: Positive predictive value: Positive predictive value is the proportion of cases giving positive test results who are already patients (3). It is the ratio of patients truly diagnosed as positive to all those who had positive test results (including healthy subjects who were incorrectly diagnosed as patient).
Detailed explanation4: For a mathematical explanation of this phenomenon, we can calculate the positive predictive value (PPV) as follows: PPV = (sensitivity x prevalence) / [ (sensitivity x prevalence) + ((1 – specificity) x (1 – prevalence)) ]