Question

Assuming a total sample of 1079 persons, among which 520 persons are having autism and 559 are healthy persons. When we pass the data of 520 autism patients into the KNN classifier, it correctly predicted “220” patients as autism category and the remaining patients into healthy category. Similarly, from 559 healthy persons, the KNN categorize “100” as autism patients and the remaining as healthy persons.

In the above scenario, if “autism” is considered as “positive class” and “healthy person” is considered as negative class then find the:

1. True Positive
2. True Negative
3. False Positive
4. False Negative
5. Sensitivity (True positive rate)
6. Specificity (True negative rate)
7. Accuracy
8. Precision
9. Draw the confusion matrix                               

Assuming a total sample of 1079 persons, among which 520 persons are having autism and 559 are healthy persons. When we pass the data of 520 autism patients into the KNN classifier, it correctly predicted “220” patients as autism category and the remaining patients into healthy category. Similarly, from 559 healthy persons, the KNN categorize “100” as autism patients and the remaining as healthy persons.

In the above scenario, if “autism” is considered as “positive class” and “healthy person” is considered as negative class then find the:

1. True Positive
2. True Negative
3. False Positive
4. False Negative
5. Sensitivity (True positive rate)
6. Specificity (True negative rate)
7. Accuracy
8. Precision
9. Draw the confusion matrix                               

Answer 1

Given,

               Total samples = 1079

               Actual Positive class samples (Autism patients) = 520

               Actual Negative class samples (Healthy persons) = 559

Correctly predicted patients in autism category = 220

Incorrectly predicted healthy persons in autism category = 100

Incorrectly predicted autism patients in healthy category = 300

Correctly predicted persons in Healthy category = 459

Answers:

(a) True Positive (TP):

Correctly predicted patients in autism category = 220

(b) True Negative (TN):

Correctly predicted persons in Healthy category = 459

(c) False Positive (FP):

Incorrectly predicted healthy persons in autism category = 100

(d) False Negative (FN):

Incorrectly predicted autism patients in healthy category = 300

(e) Sensitivity (True positive rate)

= TP/TP+FN = 220/220+300 = 220/520

= 0.4230 = 42.30%

(f) Specificity (True negative rate)

= TN/TN+FN = 459/459+100 = 459/559

= 0.8211 = 82.11%

(g) Accuracy

= TP+TN/Total samples = 220+459/1079 = 679/1079

= 0.6292 = 62.92%

(h) Precision

= TP/TP+FP = 220/220+100 = 220/320

= 0.6875 = 68.75%

(i) Confusion matrix

Predicted Autism Healthy Autism 220 (TP) 300 (FN) Actual Healthy 100 (FP) 459 (TN)