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'])
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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