A decision tree is built with WEKA to classify patients into positive or negative for diabetes on the following dataset from National Institute of Diabetes and Digestive and Kidney Diseases. The dataset has 4 attributes: class (the true diagnosis): negative or positive plas: Plasma glucose concentration a 2 hours in an oral glucose tolerance test mass: Body mass index (weight in kg/(height in m)^2) age: Age (years) The tree is shown below. === Classifier model (full training set) === J48 pruned tree ----------------- plas <= 127: negative (485.0/94.0) plas > 127 | mass <= 29.9 | | plas <= 145: negative (41.0/6.0) | | plas > 145 | | | age <= 25: negative (4.0) | | | age > 25 | | | | age <= 61: positive (27.0/9.0) | | | | age > 61: negative (4.0) | mass > 29.9 | | plas <= 157 | | | age <= 30: negative (50.0/23.0) | | | age > 30: positive (65.0/18.0) | | plas > 157: positive (92.0/12.0) Number of Leaves : 8 Size of the tree : 15 a. Use the WEKA output to construct a confusion matrix. (Hint: look at each leaf node to determine how many instances fall into each of the four quadrants; and aggregate results of all leaf nodes to obtain the final counts) (8%) bi_test1 pic.png TP=? FP=? FN=? TN=? b. In medical diagnosis, three metrics are commonly used: sensitivity, specificity and diagnosis accuracy. Sensitivity is defined as TP/(TP+FN) ; Specificity is defined as TN/(FP+TN); Diagnosis Accuracy is defined as the average of Sensitivity and Specificity. Calculate the Diagnosis Accuracy based on the confusion matrix above. (2%)