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# Complete the decision tree ADT The Decision Tree (DT) ADT is of the following format.

INSTRUCTIONS TO CANDIDATES

The Diabetic society has hired you to design a system that helps them predict the likelihood of someone having a heart attack based on their medical history. Your supervisor created a decision tree (DT) specifying how to predict a person’s risk of having a heart attack. image He left you with these instructions: Complete the decision tree ADT Generate the decision tree (DT) based on the image given. Complete the recursive program that predicts a person’s risk level based on their medical history. .

1. Complete the decision tree ADT The Decision Tree (DT) ADT is of the following format. A tuple that contains a DT string in the first index position and list in the second index position containing the root, left subtree and right subtree in that order. The decision tree ADT has the following function def make_empty_tree(root): def makeTree(root, left, right): def root(DT): def left_subtree(DT): def right_subtree(DT): def is_empty(DT):

def is_DT(DT): A DT is considered empty if both subtrees are empty lists. ("DT", [ root, [], [] ]) – empty DT .

2. Generate the decision tree (DT) based on the image given. Generate the decision tree (DT) using the ADT constructors. The tree shouldbe called . .

3. Complete the recursive program that predicts a person’s risk level based on their medical history Complete the recursive function that predicts a person’s risk level based on their medical history. The function accepts a tree and patient history as a list of 7 strings of "True" or "False". A person’s history is given in the following format. History = HGC,DB,HBP,DBF,EH,SM,BMI where each value is a string "True" or "False" Found High Glucose - HGC Diabetic - DB High Blood Pressure - HBP Diabetic Family - DBF Excess Hunger - EH Smoke - SM Body Max Index - BMI The program receives historical record of a patient in the order specified. E.g. history = [True,True,True,True,False,True,False] means that the following are known for that patient HGC = True, DB = True, HBP = True, DBF = True, EH = False, SM = True, BMI = False Based on the tree and patient history received, the function returns the patient's risk level as a 3-character string "LRL", "MRL" or "HRL", representing Low Risk Level, Moderate Risk Level or High Risk Level respectively. Low Risk Level - LRL Moderate Risk Level - MRL High Risk Level – HRL Input Format "True" "True" "True" "True" "False" "True" "False" Note that these are strings! Constraints None Output Format String with one of the following: Low Risk Level – LRL, Moderate Risk Level – MRL or High Risk Level – HRL Sample Input 0 True True True True False True False Sample Output 0 HRL Explanation 0 HGC = True DB = True HBP = True DBF = True EH = False SM = True BMI = False Based on the DT if someone has high glucose levels and they are Diabetic they are at a high risk of heart attack. (HRL) Sample Input 1 False False True True False True False Sample Output 1 MRL Explanation 1 HGC = False DB = False HBP = True DBF = True EH = False SM = True BMI = False Based on the DT if someone has low glucose levels but their blood pressure is high and their family has a history of diabetes but their BMI is less than 25 they have a moderate risk level (MRL)

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