The document describes a dataset of 1568 women that includes measurements like number of pregnancies, glucose levels, blood pressure, etc. to predict whether a subject has diabetes or not. A classification tree was fitted to the data to provide a diagnosis for subjects using the medical variables. The tree is evaluated for its accuracy, sensitivity and specificity on this dataset. Users are also asked to provide diagnoses for new subjects based on their measurements using the classification tree.

1. Data regarding a sample of 1568 women have been collected with the purpose of predicting
diagnostically whether a
subject has diabetes or not, based on certain diagnostic measurements and other physical and
medical variables. The
target variable diabetes records whether the patient has tested positive (pos) or negative (neg)
for diabetes. The
predictors record several numerical variables, which are listed below:
pregnant Number of times pregnant.
glucose Plasma glucose concentration.
pressure Diastolic blood pressure (mm Hg).
triceps Triceps skin fold thickness (mm).
insulin 2-Hour serum insulin (mu U/ml).
mass Body mass index.
pedigree Diabetes pedigree function
age Age (years)
A classification tree was fitted to the data with the purpose of providing a diagnosis for a subject
(having or not having
diabetes), using the available medical and diagnostic measurements. Below a visual
representation and the associated
output (note that the node numbering of the tree does not match the node numbering in the
output). 1. Using the information above, compute the classification accuracy, the sensitivity, and
the specificity of the fitted tree for this dataset. (30 marks) 2. Using the information from the plot
and the fitted classification tree, provide a diagnosis for each one of the subjects below. (10
marks)