Bayesian network

Write a program to construct aBayesian 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.


import numpy as np
import pandas as pd
import csv
from pgmpy.estimators import MaximumLikelihoodEstimator
from pgmpy.models import BayesianModel
from pgmpy.inference import VariableElimination

#Read the attributes
lines = list(csv.reader(open('Prog 7b-data7_names.csv', 'r')));
attributes = lines[0]
#attributes = ['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach', 'exang',
# 'oldpeak', 'slope', 'ca', 'thal', 'heartdisease']

heartDisease = pd.read_csv('Prog 7a-data7_heart.csv', names = attributes)
heartDisease = heartDisease.replace('?', np.nan)

# Display the data
print('Few examples from the dataset are given below')
print(heartDisease.head())
print('\nAttributes and datatypes')
print(heartDisease.dtypes)

# Model Baysian Network
model = BayesianModel([('age', 'trestbps'), ('age', 'fbs'), ('sex', 'trestbps'), ('sex', 'trestbps'),
('exang', 'trestbps'),('trestbps','heartdisease'),('fbs','heartdisease'),
('heartdisease','restecg'),('heartdisease','thalach'),('heartdisease','chol')])

# Learning CPDs using Maximum Likelihood Estimators
print('\nLearning CPDs using Maximum Likelihood Estimators...');
model.fit(heartDisease, estimator=MaximumLikelihoodEstimator)

# Inferencing with Bayesian Network
print('\nInferencing with Bayesian Network:')
HeartDisease_infer = VariableElimination(model)

# Computing the probability of bronc given smoke.
print('\n1.Probability of HeartDisease given Age=20')
q = HeartDisease_infer.query(variables=['heartdisease'], evidence={'age': 28})
print(q['heartdisease'])
print('\n2. Probability of HeartDisease given chol (Cholestoral) =100')
q = HeartDisease_infer.query(variables=['heartdisease'], evidence={'chol': 100})
print(q['heartdisease'])

Output->

Few examples from the dataset are given below
   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \
0   63    1   1       145   233    1        2      150      0      2.3      3   
1   67    1   4       160   286    0        2      108      1      1.5      2   
2   67    1   4       120   229    0        2      129      1      2.6      2   
3   37    1   3       130   250    0        0      187      0      3.5      3   
4   41    0   2       130   204    0        2      172      0      1.4      1   

  ca thal  heartdisease  
0  0    6             0  
1  3    3             2  
2  2    7             1  
3  0    3             0  
4  0    3             0  
Attributes and datatypes
age               int64
sex               int64
cp                int64
trestbps          int64
chol              int64
fbs               int64
restecg           int64
thalach           int64
exang             int64
oldpeak         float64
slope             int64
ca               object
thal             object
heartdisease      int64
dtype: object

Learning CPDs using Maximum Likelihood Estimators...

Inferencing with Bayesian Network:

1.Probability of HeartDisease given Age=20
╒════════════════╤═════════════════════╕
│ heartdisease   │   phi(heartdisease) │
╞════════════════╪═════════════════════╡
│ heartdisease_0 │              0.6597 │
├────────────────┼─────────────────────┤
│ heartdisease_1 │              0.1178 │
├────────────────┼─────────────────────┤
│ heartdisease_2 │              0.1064 │
├────────────────┼─────────────────────┤
│ heartdisease_3 │              0.0919 │
├────────────────┼─────────────────────┤
│ heartdisease_4 │              0.0242 │
╘════════════════╧═════════════════════╛

2. Probability of HeartDisease given chol (Cholestoral) =100
╒════════════════╤═════════════════════╕
│ heartdisease   │   phi(heartdisease) │
╞════════════════╪═════════════════════╡
│ heartdisease_0 │              0.5322 │
├────────────────┼─────────────────────┤
│ heartdisease_1 │              0.1565 │
├────────────────┼─────────────────────┤
│ heartdisease_2 │              0.1360 │
├────────────────┼─────────────────────┤
│ heartdisease_3 │              0.1258 │
├────────────────┼─────────────────────┤
│ heartdisease_4 │              0.0496 │
╘════════════════╧═════════════════════╛

Dataset1->
63,1,1,145,233,1,2,150,0,2.3,3,0,6,0
67,1,4,160,286,0,2,108,1,1.5,2,3,3,2
67,1,4,120,229,0,2,129,1,2.6,2,2,7,1
37,1,3,130,250,0,0,187,0,3.5,3,0,3,0
41,0,2,130,204,0,2,172,0,1.4,1,0,3,0
56,1,2,120,236,0,0,178,0,0.8,1,0,3,0
62,0,4,140,268,0,2,160,0,3.6,3,2,3,3
57,0,4,120,354,0,0,163,1,0.6,1,0,3,0
63,1,4,130,254,0,2,147,0,1.4,2,1,7,2
53,1,4,140,203,1,2,155,1,3.1,3,0,7,1
57,1,4,140,192,0,0,148,0,0.4,2,0,6,0
56,0,2,140,294,0,2,153,0,1.3,2,0,3,0
56,1,3,130,256,1,2,142,1,0.6,2,1,6,2
44,1,2,120,263,0,0,173,0,0,1,0,7,0
52,1,3,172,199,1,0,162,0,0.5,1,0,7,0
57,1,3,150,168,0,0,174,0,1.6,1,0,3,0
48,1,2,110,229,0,0,168,0,1,3,0,7,1
54,1,4,140,239,0,0,160,0,1.2,1,0,3,0
48,0,3,130,275,0,0,139,0,0.2,1,0,3,0
49,1,2,130,266,0,0,171,0,0.6,1,0,3,0
64,1,1,110,211,0,2,144,1,1.8,2,0,3,0
58,0,1,150,283,1,2,162,0,1,1,0,3,0
58,1,2,120,284,0,2,160,0,1.8,2,0,3,1
58,1,3,132,224,0,2,173,0,3.2,1,2,7,3
60,1,4,130,206,0,2,132,1,2.4,2,2,7,4
50,0,3,120,219,0,0,158,0,1.6,2,0,3,0
58,0,3,120,340,0,0,172,0,0,1,0,3,0
66,0,1,150,226,0,0,114,0,2.6,3,0,3,0
43,1,4,150,247,0,0,171,0,1.5,1,0,3,0
40,1,4,110,167,0,2,114,1,2,2,0,7,3
69,0,1,140,239,0,0,151,0,1.8,1,2,3,0
60,1,4,117,230,1,0,160,1,1.4,1,2,7,2
64,1,3,140,335,0,0,158,0,0,1,0,3,1
59,1,4,135,234,0,0,161,0,0.5,2,0,7,0
44,1,3,130,233,0,0,179,1,0.4,1,0,3,0
42,1,4,140,226,0,0,178,0,0,1,0,3,0
43,1,4,120,177,0,2,120,1,2.5,2,0,7,3
57,1,4,150,276,0,2,112,1,0.6,2,1,6,1
55,1,4,132,353,0,0,132,1,1.2,2,1,7,3
61,1,3,150,243,1,0,137,1,1,2,0,3,0
65,0,4,150,225,0,2,114,0,1,2,3,7,4
40,1,1,140,199,0,0,178,1,1.4,1,0,7,0
71,0,2,160,302,0,0,162,0,0.4,1,2,3,0
59,1,3,150,212,1,0,157,0,1.6,1,0,3,0
61,0,4,130,330,0,2,169,0,0,1,0,3,1
58,1,3,112,230,0,2,165,0,2.5,2,1,7,4
51,1,3,110,175,0,0,123,0,0.6,1,0,3,0
50,1,4,150,243,0,2,128,0,2.6,2,0,7,4
65,0,3,140,417,1,2,157,0,0.8,1,1,3,0
53,1,3,130,197,1,2,152,0,1.2,3,0,3,0
41,0,2,105,198,0,0,168,0,0,1,1,3,0
65,1,4,120,177,0,0,140,0,0.4,1,0,7,0
44,1,4,112,290,0,2,153,0,0,1,1,3,2
44,1,2,130,219,0,2,188,0,0,1,0,3,0
60,1,4,130,253,0,0,144,1,1.4,1,1,7,1
54,1,4,124,266,0,2,109,1,2.2,2,1,7,1
50,1,3,140,233,0,0,163,0,0.6,2,1,7,1
41,1,4,110,172,0,2,158,0,0,1,0,7,1
54,1,3,125,273,0,2,152,0,0.5,3,1,3,0
51,1,1,125,213,0,2,125,1,1.4,1,1,3,0
51,0,4,130,305,0,0,142,1,1.2,2,0,7,2
46,0,3,142,177,0,2,160,1,1.4,3,0,3,0
58,1,4,128,216,0,2,131,1,2.2,2,3,7,1
54,0,3,135,304,1,0,170,0,0,1,0,3,0
54,1,4,120,188,0,0,113,0,1.4,2,1,7,2
60,1,4,145,282,0,2,142,1,2.8,2,2,7,2
60,1,3,140,185,0,2,155,0,3,2,0,3,1
54,1,3,150,232,0,2,165,0,1.6,1,0,7,0
59,1,4,170,326,0,2,140,1,3.4,3,0,7,2
46,1,3,150,231,0,0,147,0,3.6,2,0,3,1
65,0,3,155,269,0,0,148,0,0.8,1,0,3,0
67,1,4,125,254,1,0,163,0,0.2,2,2,7,3
62,1,4,120,267,0,0,99,1,1.8,2,2,7,1
65,1,4,110,248,0,2,158,0,0.6,1,2,6,1
44,1,4,110,197,0,2,177,0,0,1,1,3,1
65,0,3,160,360,0,2,151,0,0.8,1,0,3,0
60,1,4,125,258,0,2,141,1,2.8,2,1,7,1
51,0,3,140,308,0,2,142,0,1.5,1,1,3,0
48,1,2,130,245,0,2,180,0,0.2,2,0,3,0
58,1,4,150,270,0,2,111,1,0.8,1,0,7,3
45,1,4,104,208,0,2,148,1,3,2,0,3,0
53,0,4,130,264,0,2,143,0,0.4,2,0,3,0
39,1,3,140,321,0,2,182,0,0,1,0,3,0
68,1,3,180,274,1,2,150,1,1.6,2,0,7,3
52,1,2,120,325,0,0,172,0,0.2,1,0,3,0
44,1,3,140,235,0,2,180,0,0,1,0,3,0
47,1,3,138,257,0,2,156,0,0,1,0,3,0
53,0,3,128,216,0,2,115,0,0,1,0,?,0
53,0,4,138,234,0,2,160,0,0,1,0,3,0
51,0,3,130,256,0,2,149,0,0.5,1,0,3,0
66,1,4,120,302,0,2,151,0,0.4,2,0,3,0
62,0,4,160,164,0,2,145,0,6.2,3,3,7,3
62,1,3,130,231,0,0,146,0,1.8,2,3,7,0
44,0,3,108,141,0,0,175,0,0.6,2,0,3,0
63,0,3,135,252,0,2,172,0,0,1,0,3,0
52,1,4,128,255,0,0,161,1,0,1,1,7,1
59,1,4,110,239,0,2,142,1,1.2,2,1,7,2
60,0,4,150,258,0,2,157,0,2.6,2,2,7,3
52,1,2,134,201,0,0,158,0,0.8,1,1,3,0
48,1,4,122,222,0,2,186,0,0,1,0,3,0
45,1,4,115,260,0,2,185,0,0,1,0,3,0
34,1,1,118,182,0,2,174,0,0,1,0,3,0
57,0,4,128,303,0,2,159,0,0,1,1,3,0
71,0,3,110,265,1,2,130,0,0,1,1,3,0
49,1,3,120,188,0,0,139,0,2,2,3,7,3
54,1,2,108,309,0,0,156,0,0,1,0,7,0
59,1,4,140,177,0,0,162,1,0,1,1,7,2
57,1,3,128,229,0,2,150,0,0.4,2,1,7,1
61,1,4,120,260,0,0,140,1,3.6,2,1,7,2
39,1,4,118,219,0,0,140,0,1.2,2,0,7,3
61,0,4,145,307,0,2,146,1,1,2,0,7,1
56,1,4,125,249,1,2,144,1,1.2,2,1,3,1
52,1,1,118,186,0,2,190,0,0,2,0,6,0
43,0,4,132,341,1,2,136,1,3,2,0,7,2
62,0,3,130,263,0,0,97,0,1.2,2,1,7,2
41,1,2,135,203,0,0,132,0,0,2,0,6,0
58,1,3,140,211,1,2,165,0,0,1,0,3,0
35,0,4,138,183,0,0,182,0,1.4,1,0,3,0
63,1,4,130,330,1,2,132,1,1.8,1,3,7,3
65,1,4,135,254,0,2,127,0,2.8,2,1,7,2
48,1,4,130,256,1,2,150,1,0,1,2,7,3
63,0,4,150,407,0,2,154,0,4,2,3,7,4
51,1,3,100,222,0,0,143,1,1.2,2,0,3,0
55,1,4,140,217,0,0,111,1,5.6,3,0,7,3
65,1,1,138,282,1,2,174,0,1.4,2,1,3,1
45,0,2,130,234,0,2,175,0,0.6,2,0,3,0
56,0,4,200,288,1,2,133,1,4,3,2,7,3
54,1,4,110,239,0,0,126,1,2.8,2,1,7,3
44,1,2,120,220,0,0,170,0,0,1,0,3,0
62,0,4,124,209,0,0,163,0,0,1,0,3,0
54,1,3,120,258,0,2,147,0,0.4,2,0,7,0
51,1,3,94,227,0,0,154,1,0,1,1,7,0
29,1,2,130,204,0,2,202,0,0,1,0,3,0
51,1,4,140,261,0,2,186,1,0,1,0,3,0
43,0,3,122,213,0,0,165,0,0.2,2,0,3,0
55,0,2,135,250,0,2,161,0,1.4,2,0,3,0
70,1,4,145,174,0,0,125,1,2.6,3,0,7,4
62,1,2,120,281,0,2,103,0,1.4,2,1,7,3
35,1,4,120,198,0,0,130,1,1.6,2,0,7,1
51,1,3,125,245,1,2,166,0,2.4,2,0,3,0
59,1,2,140,221,0,0,164,1,0,1,0,3,0
59,1,1,170,288,0,2,159,0,0.2,2,0,7,1
52,1,2,128,205,1,0,184,0,0,1,0,3,0
64,1,3,125,309,0,0,131,1,1.8,2,0,7,1
58,1,3,105,240,0,2,154,1,0.6,2,0,7,0
47,1,3,108,243,0,0,152,0,0,1,0,3,1
57,1,4,165,289,1,2,124,0,1,2,3,7,4
41,1,3,112,250,0,0,179,0,0,1,0,3,0
45,1,2,128,308,0,2,170,0,0,1,0,3,0
60,0,3,102,318,0,0,160,0,0,1,1,3,0
52,1,1,152,298,1,0,178,0,1.2,2,0,7,0
42,0,4,102,265,0,2,122,0,0.6,2,0,3,0
67,0,3,115,564,0,2,160,0,1.6,2,0,7,0
55,1,4,160,289,0,2,145,1,0.8,2,1,7,4
64,1,4,120,246,0,2,96,1,2.2,3,1,3,3
70,1,4,130,322,0,2,109,0,2.4,2,3,3,1
51,1,4,140,299,0,0,173,1,1.6,1,0,7,1
58,1,4,125,300,0,2,171,0,0,1,2,7,1
60,1,4,140,293,0,2,170,0,1.2,2,2,7,2
68,1,3,118,277,0,0,151,0,1,1,1,7,0
46,1,2,101,197,1,0,156,0,0,1,0,7,0
77,1,4,125,304,0,2,162,1,0,1,3,3,4
54,0,3,110,214,0,0,158,0,1.6,2,0,3,0
58,0,4,100,248,0,2,122,0,1,2,0,3,0
48,1,3,124,255,1,0,175,0,0,1,2,3,0
57,1,4,132,207,0,0,168,1,0,1,0,7,0
52,1,3,138,223,0,0,169,0,0,1,?,3,0
54,0,2,132,288,1,2,159,1,0,1,1,3,0
35,1,4,126,282,0,2,156,1,0,1,0,7,1
45,0,2,112,160,0,0,138,0,0,2,0,3,0
70,1,3,160,269,0,0,112,1,2.9,2,1,7,3
53,1,4,142,226,0,2,111,1,0,1,0,7,0
59,0,4,174,249,0,0,143,1,0,2,0,3,1
62,0,4,140,394,0,2,157,0,1.2,2,0,3,0
64,1,4,145,212,0,2,132,0,2,2,2,6,4
57,1,4,152,274,0,0,88,1,1.2,2,1,7,1
52,1,4,108,233,1,0,147,0,0.1,1,3,7,0
56,1,4,132,184,0,2,105,1,2.1,2,1,6,1
43,1,3,130,315,0,0,162,0,1.9,1,1,3,0
53,1,3,130,246,1,2,173,0,0,1,3,3,0
48,1,4,124,274,0,2,166,0,0.5,2,0,7,3
56,0,4,134,409,0,2,150,1,1.9,2,2,7,2
42,1,1,148,244,0,2,178,0,0.8,1,2,3,0
59,1,1,178,270,0,2,145,0,4.2,3,0,7,0
60,0,4,158,305,0,2,161,0,0,1,0,3,1
63,0,2,140,195,0,0,179,0,0,1,2,3,0
42,1,3,120,240,1,0,194,0,0.8,3,0,7,0
66,1,2,160,246,0,0,120,1,0,2,3,6,2
54,1,2,192,283,0,2,195,0,0,1,1,7,1
69,1,3,140,254,0,2,146,0,2,2,3,7,2
50,1,3,129,196,0,0,163,0,0,1,0,3,0
51,1,4,140,298,0,0,122,1,4.2,2,3,7,3
43,1,4,132,247,1,2,143,1,0.1,2,?,7,1
62,0,4,138,294,1,0,106,0,1.9,2,3,3,2
68,0,3,120,211,0,2,115,0,1.5,2,0,3,0
67,1,4,100,299,0,2,125,1,0.9,2,2,3,3
69,1,1,160,234,1,2,131,0,0.1,2,1,3,0
45,0,4,138,236,0,2,152,1,0.2,2,0,3,0
50,0,2,120,244,0,0,162,0,1.1,1,0,3,0
59,1,1,160,273,0,2,125,0,0,1,0,3,1
50,0,4,110,254,0,2,159,0,0,1,0,3,0
64,0,4,180,325,0,0,154,1,0,1,0,3,0
57,1,3,150,126,1,0,173,0,0.2,1,1,7,0
64,0,3,140,313,0,0,133,0,0.2,1,0,7,0
43,1,4,110,211,0,0,161,0,0,1,0,7,0
45,1,4,142,309,0,2,147,1,0,2,3,7,3
58,1,4,128,259,0,2,130,1,3,2,2,7,3
50,1,4,144,200,0,2,126,1,0.9,2,0,7,3
55,1,2,130,262,0,0,155,0,0,1,0,3,0
62,0,4,150,244,0,0,154,1,1.4,2,0,3,1
37,0,3,120,215,0,0,170,0,0,1,0,3,0
38,1,1,120,231,0,0,182,1,3.8,2,0,7,4
41,1,3,130,214,0,2,168,0,2,2,0,3,0
66,0,4,178,228,1,0,165,1,1,2,2,7,3
52,1,4,112,230,0,0,160,0,0,1,1,3,1
56,1,1,120,193,0,2,162,0,1.9,2,0,7,0
46,0,2,105,204,0,0,172,0,0,1,0,3,0
46,0,4,138,243,0,2,152,1,0,2,0,3,0
64,0,4,130,303,0,0,122,0,2,2,2,3,0
59,1,4,138,271,0,2,182,0,0,1,0,3,0
41,0,3,112,268,0,2,172,1,0,1,0,3,0
54,0,3,108,267,0,2,167,0,0,1,0,3,0
39,0,3,94,199,0,0,179,0,0,1,0,3,0
53,1,4,123,282,0,0,95,1,2,2,2,7,3
63,0,4,108,269,0,0,169,1,1.8,2,2,3,1
34,0,2,118,210,0,0,192,0,0.7,1,0,3,0
47,1,4,112,204,0,0,143,0,0.1,1,0,3,0
67,0,3,152,277,0,0,172,0,0,1,1,3,0
54,1,4,110,206,0,2,108,1,0,2,1,3,3
66,1,4,112,212,0,2,132,1,0.1,1,1,3,2
52,0,3,136,196,0,2,169,0,0.1,2,0,3,0
55,0,4,180,327,0,1,117,1,3.4,2,0,3,2
49,1,3,118,149,0,2,126,0,0.8,1,3,3,1
74,0,2,120,269,0,2,121,1,0.2,1,1,3,0
54,0,3,160,201,0,0,163,0,0,1,1,3,0
54,1,4,122,286,0,2,116,1,3.2,2,2,3,3
56,1,4,130,283,1,2,103,1,1.6,3,0,7,2
46,1,4,120,249,0,2,144,0,0.8,1,0,7,1
49,0,2,134,271,0,0,162,0,0,2,0,3,0
42,1,2,120,295,0,0,162,0,0,1,0,3,0
41,1,2,110,235,0,0,153,0,0,1,0,3,0
41,0,2,126,306,0,0,163,0,0,1,0,3,0
49,0,4,130,269,0,0,163,0,0,1,0,3,0
61,1,1,134,234,0,0,145,0,2.6,2,2,3,2
60,0,3,120,178,1,0,96,0,0,1,0,3,0
67,1,4,120,237,0,0,71,0,1,2,0,3,2
58,1,4,100,234,0,0,156,0,0.1,1,1,7,2
47,1,4,110,275,0,2,118,1,1,2,1,3,1
52,1,4,125,212,0,0,168,0,1,1,2,7,3
62,1,2,128,208,1,2,140,0,0,1,0,3,0
57,1,4,110,201,0,0,126,1,1.5,2,0,6,0
58,1,4,146,218,0,0,105,0,2,2,1,7,1
64,1,4,128,263,0,0,105,1,0.2,2,1,7,0
51,0,3,120,295,0,2,157,0,0.6,1,0,3,0
43,1,4,115,303,0,0,181,0,1.2,2,0,3,0
42,0,3,120,209,0,0,173,0,0,2,0,3,0
67,0,4,106,223,0,0,142,0,0.3,1,2,3,0
76,0,3,140,197,0,1,116,0,1.1,2,0,3,0
70,1,2,156,245,0,2,143,0,0,1,0,3,0
57,1,2,124,261,0,0,141,0,0.3,1,0,7,1
44,0,3,118,242,0,0,149,0,0.3,2,1,3,0
58,0,2,136,319,1,2,152,0,0,1,2,3,3
60,0,1,150,240,0,0,171,0,0.9,1,0,3,0
44,1,3,120,226,0,0,169,0,0,1,0,3,0
61,1,4,138,166,0,2,125,1,3.6,2,1,3,4
42,1,4,136,315,0,0,125,1,1.8,2,0,6,2
52,1,4,128,204,1,0,156,1,1,2,0,?,2
59,1,3,126,218,1,0,134,0,2.2,2,1,6,2
40,1,4,152,223,0,0,181,0,0,1,0,7,1
42,1,3,130,180,0,0,150,0,0,1,0,3,0
61,1,4,140,207,0,2,138,1,1.9,1,1,7,1
66,1,4,160,228,0,2,138,0,2.3,1,0,6,0
46,1,4,140,311,0,0,120,1,1.8,2,2,7,2
71,0,4,112,149,0,0,125,0,1.6,2,0,3,0
59,1,1,134,204,0,0,162,0,0.8,1,2,3,1
64,1,1,170,227,0,2,155,0,0.6,2,0,7,0
66,0,3,146,278,0,2,152,0,0,2,1,3,0
39,0,3,138,220,0,0,152,0,0,2,0,3,0
57,1,2,154,232,0,2,164,0,0,1,1,3,1
58,0,4,130,197,0,0,131,0,0.6,2,0,3,0
57,1,4,110,335,0,0,143,1,3,2,1,7,2
47,1,3,130,253,0,0,179,0,0,1,0,3,0
55,0,4,128,205,0,1,130,1,2,2,1,7,3
35,1,2,122,192,0,0,174,0,0,1,0,3,0
61,1,4,148,203,0,0,161,0,0,1,1,7,2
58,1,4,114,318,0,1,140,0,4.4,3,3,6,4
58,0,4,170,225,1,2,146,1,2.8,2,2,6,2
58,1,2,125,220,0,0,144,0,0.4,2,?,7,0
56,1,2,130,221,0,2,163,0,0,1,0,7,0
56,1,2,120,240,0,0,169,0,0,3,0,3,0
67,1,3,152,212,0,2,150,0,0.8,2,0,7,1
55,0,2,132,342,0,0,166,0,1.2,1,0,3,0
44,1,4,120,169,0,0,144,1,2.8,3,0,6,2
63,1,4,140,187,0,2,144,1,4,1,2,7,2
63,0,4,124,197,0,0,136,1,0,2,0,3,1
41,1,2,120,157,0,0,182,0,0,1,0,3,0
59,1,4,164,176,1,2,90,0,1,2,2,6,3
57,0,4,140,241,0,0,123,1,0.2,2,0,7,1
45,1,1,110,264,0,0,132,0,1.2,2,0,7,1
68,1,4,144,193,1,0,141,0,3.4,2,2,7,2
57,1,4,130,131,0,0,115,1,1.2,2,1,7,3
57,0,2,130,236,0,2,174,0,0,2,1,3,1
38,1,3,138,175,0,0,173,0,0,1,?,3,0


Dataset 2->
age,sex,cp,trestbps,chol,fbs,restecg,thalach,exang,oldpeak,slope,ca,thal,heartdisease

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