ML 7 - A BAYESIAN NETWORK

7. WRITE A PROGRAM TO CONSTRUCT A BAYESIAN NETWORK CONSIDERING MEDICAL DATA. USE THIS MODEL TO DEMONSTRATE THE DIAGNOSIS OF HEART PATIENTS USING STANDARD HEART DISEASE DATA SET. YOU CAN USE JAVA/PYTHON ML LIBRARY CLASSES/API.

 SOLUTION 

To execute the program first open a terminal and install packages "pgmpy" & "bayespy"
in Terminal
>>>pip install pgmpy
>>>pip install bayespy

heartdisease.csv

SuperSeniorCitizen	Male	Yes	Medium	Sedetary	High	Yes
SuperSeniorCitizen	Female	Yes	Medium	Sedetary	High	Yes
SeniorCitizen	Male	No	High	Moderate	BorderLine	Yes
Teen	Male	Yes	Medium	Sedetary	Normal	No
Youth	Female	Yes	High	Athlete	Normal	No
MiddleAged	Male	Yes	Medium	Active	High	Yes
Teen	Male	Yes	High	Moderate	High	Yes
SuperSeniorCitizen	Male	Yes	Medium	Sedetary	High	Yes
Youth	Female	Yes	High	Athlete	Normal	No
SeniorCitizen	Female	No	High	Athlete	Normal	Yes
Teen	Female	No	Medium	Moderate	High	Yes
Teen	Male	Yes	Medium	Sedetary	Normal	No
MiddleAged	Female	No	High	Athlete	High	No
MiddleAged	Male	Yes	Medium	Active	High	Yes
Youth	Female	Yes	High	Athlete	BorderLine	No
SuperSeniorCitizen	Male	Yes	High	Athlete	Normal	Yes
SeniorCitizen	Female	No	Medium	Moderate	BorderLine	Yes
Youth	Female	Yes	Medium	Athlete	BorderLine	No
Teen	Male	Yes	Medium	Sedetary	Normal	No

lab7.py - PROGRAM SOLUTION 2 (Given by Lokesh sir)

import bayespy as bp
import numpy as np
import csv
from colorama import init
from colorama import Fore, Back, Style
init()

ageEnum = {'SuperSeniorCitizen':0, 'SeniorCitizen':1, 'MiddleAged':2, 'Youth':3, 'Teen':4}
genderEnum = {'Male':0, 'Female':1}
familyHistoryEnum = {'Yes':0, 'No':1}
dietEnum = {'High':0, 'Medium':1, 'Low':2}
lifeStyleEnum = {'Athlete':0, 'Active':1, 'Moderate':2, 'Sedetary':3}
cholesterolEnum = {'High':0, 'BorderLine':1, 'Normal':2}
heartDiseaseEnum = {'Yes':0, 'No':1}

with open('heartdisease.csv') as csvfile:
lines = csv.reader(csvfile)
dataset = list(lines)
data = []
for x in dataset:
data.append([ageEnum[x[0]],genderEnum[x[1]],familyHistoryEnum[x[2]],dietEnum[x[3]],lifeStyleEnum[x[4]],cholesterolEnum[x[5]],heartDiseaseEnum[x[6]]])

data = np.array(data)
N = len(data)

p_age = bp.nodes.Dirichlet(1.0*np.ones(5))
age = bp.nodes.Categorical(p_age, plates=(N,))

age.observe(data[:,0])
p_gender = bp.nodes.Dirichlet(1.0*np.ones(2))
gender = bp.nodes.Categorical(p_gender, plates=(N,))

gender.observe(data[:,1])
p_familyhistory = bp.nodes.Dirichlet(1.0*np.ones(2))
familyhistory = bp.nodes.Categorical(p_familyhistory, plates=(N,))

familyhistory.observe(data[:,2])
p_diet = bp.nodes.Dirichlet(1.0*np.ones(3))
diet = bp.nodes.Categorical(p_diet, plates=(N,))

diet.observe(data[:,3])
p_lifestyle = bp.nodes.Dirichlet(1.0*np.ones(4))
lifestyle = bp.nodes.Categorical(p_lifestyle, plates=(N,))

lifestyle.observe(data[:,4])
p_cholesterol = bp.nodes.Dirichlet(1.0*np.ones(3))
cholesterol = bp.nodes.Categorical(p_cholesterol, plates=(N,))

cholesterol.observe(data[:,5])
p_heartdisease = bp.nodes.Dirichlet(np.ones(2), plates=(5, 2, 2, 3, 4, 3))
heartdisease = bp.nodes.MultiMixture([age, gender, familyhistory, diet, lifestyle, cholesterol], bp.nodes.Categorical, p_heartdisease)

heartdisease.observe(data[:,6])
p_heartdisease.update()

m = 0
while m == 0:
print("\n")
res = bp.nodes.MultiMixture(
[int(input('Enter Age: ' + str(ageEnum))),
int(input('Enter Gender: ' + str(genderEnum))),
int(input('Enter FamilyHistory: ' + str(familyHistoryEnum))),
int(input('Enter dietEnum: ' + str(dietEnum))),
int(input('Enter LifeStyle: ' + str(lifeStyleEnum))),
int(input('Enter Cholesterol: ' + str(cholesterolEnum)))],
bp.nodes.Categorical, p_heartdisease).get_moments()[0][heartDiseaseEnum['Yes']]
print("Probability(HeartDisease) = " + str(res))
m = int(input("Enter for Continue:0, Exit :1 "))

STEPS & OUTPUT:

to view steps & output click HERE