Create 7.py

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.

Author: fatalcoder524

7.py

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+import numpy as np
+import csv
+import pandas as pd
+from pgmpy.models import BayesianModel
+from pgmpy.estimators import MaximumLikelihoodEstimator
+from pgmpy.inference import VariableElimination
+heartDisease = pd.read_csv('heart.csv')
+heartDisease = heartDisease.replace('?',np.nan)
+print('Few examples from the dataset are given below')
+print(heartDisease.head())
+model = BayesianModel([('age','trestbps'),('age','fbs'),('sex','trestbps'),('exang','trestbps'),('trestbps','heartdisease'),('fbs','heartdisease'),('heartdisease','restecg'),('heartdisease','thalach'),('heartdisease','chol')])
+print('\nLearning CPD using Maximum likelihood estimators')
+model.fit(heartDisease,estimator=MaximumLikelihoodEstimator)
+print('\n Inferencing with Bayesian Network:')
+HeartDisease_infer = VariableElimination(model)
+print('\n 1. Probability of HeartDisease given Age=28')
+q=HeartDisease_infer.query(variables=['heartdisease'],evidence={'age':28})
+print(q['heartdisease'])
+print('\n 2. Probability of HeartDisease given cholesterol=100')
+q=HeartDisease_infer.query(variables=['heartdisease'],evidence={'chol':100})
+print(q['heartdisease'])