Heart Disease Machine Learning Model¶

hearthealth.jpg

In [1]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
import warnings

Key for Data¶

age: The person’s age in years

sex: The person’s sex (1 = male, 0 = female)

cp: chest pain type ( Value 0: asymptomatic Value 1: atypical angina Value 2: non-anginal pain Value 3: typical angina )

trestbps: The person’s resting blood pressure (mm Hg on admission to the hospital)

chol: The person’s cholesterol measurement in mg/dl

fbs: The person’s fasting blood sugar (> 120 mg/dl, 1 = true; 0 = false)

restecg: resting electrocardiographic results ( Value 0: showing probable or definite left ventricular hypertrophy by Estes’ criteria Value 1: normal Value 2: having ST-T wave abnormality )

thalach: The person’s maximum heart rate achieved

exang: Exercise induced angina (1 = yes; 0 = no)

oldpeak: ST depression induced by exercise relative to rest

slope: the slope of the peak exercise ST segment ( 0: downsloping; 1: flat; 2: upsloping )

ca: The number of major vessels (0–3)

thal: A blood disorder called thalassemia ( Value 1: fixed defect Value 2: normal blood flow Value 3: reversible defect )

target: Heart disease (0 = no, 1 = yes)

Processing and Inspecting the Data¶

In [2]:
heart_data = pd.read_csv('.../heart_disease_data.csv')
In [3]:
heart_data.head()
Out[3]:
age sex cp trestbps chol fbs restecg thalach exang oldpeak slope ca thal target
0 63 1 3 145 233 1 0 150 0 2.3 0 0 1 1
1 37 1 2 130 250 0 1 187 0 3.5 0 0 2 1
2 41 0 1 130 204 0 0 172 0 1.4 2 0 2 1
3 56 1 1 120 236 0 1 178 0 0.8 2 0 2 1
4 57 0 0 120 354 0 1 163 1 0.6 2 0 2 1
In [4]:
heart_data.tail()
Out[4]:
age sex cp trestbps chol fbs restecg thalach exang oldpeak slope ca thal target
298 57 0 0 140 241 0 1 123 1 0.2 1 0 3 0
299 45 1 3 110 264 0 1 132 0 1.2 1 0 3 0
300 68 1 0 144 193 1 1 141 0 3.4 1 2 3 0
301 57 1 0 130 131 0 1 115 1 1.2 1 1 3 0
302 57 0 1 130 236 0 0 174 0 0.0 1 1 2 0
In [5]:
heart_data.shape
Out[5]:
(303, 14)
In [6]:
heart_data.isnull().sum()
Out[6]:
age         0
sex         0
cp          0
trestbps    0
chol        0
fbs         0
restecg     0
thalach     0
exang       0
oldpeak     0
slope       0
ca          0
thal        0
target      0
dtype: int64
In [7]:
heart_data.nunique()
Out[7]:
age          41
sex           2
cp            4
trestbps     49
chol        152
fbs           2
restecg       3
thalach      91
exang         2
oldpeak      40
slope         3
ca            5
thal          4
target        2
dtype: int64
In [8]:
heart_data.dtypes
Out[8]:
age           int64
sex           int64
cp            int64
trestbps      int64
chol          int64
fbs           int64
restecg       int64
thalach       int64
exang         int64
oldpeak     float64
slope         int64
ca            int64
thal          int64
target        int64
dtype: object
In [9]:
heart_data['target'].value_counts()
Out[9]:
1    165
0    138
Name: target, dtype: int64

Distributions¶

In [10]:
plt.hist(heart_data['age'], edgecolor = 'black')
plt.title('Age')

plt.show()
In [11]:
plt.hist(heart_data['cp'], bins = [0, 1, 2, 3], edgecolor = 'black')
plt.title('Chest Pain Type')

plt.show()
In [12]:
plt.hist(heart_data['trestbps'], edgecolor = 'black')
plt.title('Resting Blood Pressure')

plt.show()
In [13]:
plt.hist(heart_data['chol'], edgecolor = 'black')
plt.title('Cholesterol')

plt.show()
In [14]:
plt.hist(heart_data['thalach'], edgecolor = 'black')
plt.title('Max Heart Rate')

plt.show()
In [15]:
plt.hist(heart_data['oldpeak'], edgecolor = 'black')
plt.title('ST Depression')

plt.show()

Data Visualizations¶

Pie Graph¶

In [16]:
# Filter the DataFrame based on the 'target' column
heart_data_positive = heart_data[heart_data['target'] == 1]

# Now, you can calculate the count for your pie chart
positive_count = len(heart_data_positive)
negative_count = len(heart_data) - positive_count

labels = ['Heart Disease', 'No Heart Disease']
sizes = [positive_count, negative_count]

colors = ["red", "lightblue"]
explode = (0.03, 0)

plt.figure(figsize=(7, 7))
plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.1f%%', startangle=90)
plt.title('Distribution of Heart Disease in the dataset')
plt.show()

Correlation Graph¶

In [17]:
import seaborn as sns

correlation_matrix = heart_data.corr()

plt.figure(figsize=(10, 8))
sns.heatmap(correlation_matrix, annot=True, cmap='inferno', fmt=".2f", linewidths=0.5)
plt.title('Correlation Matrix')
plt.show()

Scatter Plot¶

In [18]:
plt.figure(figsize=(10, 8))
plt.scatter(heart_data['thalach'],heart_data['oldpeak'], s=60, c = 'orange', edgecolor = 'black', linewidth = 0.5, alpha = 1)
plt.xlabel('Max Heart Rate')
plt.ylabel('ST Depression')
plt.title('Max Heart Rate vs ST Depression')

plt.show()

Swarmplot¶

In [19]:
melted_data = pd.melt(heart_data, id_vars='target', value_vars=['trestbps','thalach'])

plt.figure(figsize=(10, 8))
sns.swarmplot(x='variable', y='value', hue='target', data=melted_data, size=5)

plt.show()

Data Analysis¶

In [20]:
heart_data
Out[20]:
age sex cp trestbps chol fbs restecg thalach exang oldpeak slope ca thal target
0 63 1 3 145 233 1 0 150 0 2.3 0 0 1 1
1 37 1 2 130 250 0 1 187 0 3.5 0 0 2 1
2 41 0 1 130 204 0 0 172 0 1.4 2 0 2 1
3 56 1 1 120 236 0 1 178 0 0.8 2 0 2 1
4 57 0 0 120 354 0 1 163 1 0.6 2 0 2 1
... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
298 57 0 0 140 241 0 1 123 1 0.2 1 0 3 0
299 45 1 3 110 264 0 1 132 0 1.2 1 0 3 0
300 68 1 0 144 193 1 1 141 0 3.4 1 2 3 0
301 57 1 0 130 131 0 1 115 1 1.2 1 1 3 0
302 57 0 1 130 236 0 0 174 0 0.0 1 1 2 0

303 rows × 14 columns

In [21]:
heart_data_positive = heart_data[heart_data['target'] == 1]

heart_data_positive
Out[21]:
age sex cp trestbps chol fbs restecg thalach exang oldpeak slope ca thal target
0 63 1 3 145 233 1 0 150 0 2.3 0 0 1 1
1 37 1 2 130 250 0 1 187 0 3.5 0 0 2 1
2 41 0 1 130 204 0 0 172 0 1.4 2 0 2 1
3 56 1 1 120 236 0 1 178 0 0.8 2 0 2 1
4 57 0 0 120 354 0 1 163 1 0.6 2 0 2 1
... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
160 56 1 1 120 240 0 1 169 0 0.0 0 0 2 1
161 55 0 1 132 342 0 1 166 0 1.2 2 0 2 1
162 41 1 1 120 157 0 1 182 0 0.0 2 0 2 1
163 38 1 2 138 175 0 1 173 0 0.0 2 4 2 1
164 38 1 2 138 175 0 1 173 0 0.0 2 4 2 1

165 rows × 14 columns

In [22]:
heart_data_negative = heart_data[heart_data['target'] == 0]

heart_data_negative
Out[22]:
age sex cp trestbps chol fbs restecg thalach exang oldpeak slope ca thal target
165 67 1 0 160 286 0 0 108 1 1.5 1 3 2 0
166 67 1 0 120 229 0 0 129 1 2.6 1 2 3 0
167 62 0 0 140 268 0 0 160 0 3.6 0 2 2 0
168 63 1 0 130 254 0 0 147 0 1.4 1 1 3 0
169 53 1 0 140 203 1 0 155 1 3.1 0 0 3 0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
298 57 0 0 140 241 0 1 123 1 0.2 1 0 3 0
299 45 1 3 110 264 0 1 132 0 1.2 1 0 3 0
300 68 1 0 144 193 1 1 141 0 3.4 1 2 3 0
301 57 1 0 130 131 0 1 115 1 1.2 1 1 3 0
302 57 0 1 130 236 0 0 174 0 0.0 1 1 2 0

138 rows × 14 columns

Average resting blood pressure of patients¶

In [23]:
bps_avg_pos = heart_data_positive['trestbps'].mean()
bps_avg_neg = heart_data_negative['trestbps'].mean()

print(f'The average resting blood pressure of a patient with a heart disease is', bps_avg_pos)
print(f'The average resting blood pressure of a patient with no heart disease is', bps_avg_neg)

labels = ['Heart Disease', 'No Heart Disease']
average_bps = [bps_avg_pos, bps_avg_neg]

plt.figure(figsize=(8, 8))
plt.bar(labels, average_bps, color=['red', 'lightgreen'])
plt.ylabel('Average Resting Blood Pressure')
plt.title('Average Resting Blood Pressure for Patients')
plt.ylim(50, 140)

plt.show()

The average resting blood pressure of a patient with a heart disease is 129.3030303030303
The average resting blood pressure of a patient with no heart disease is 134.3985507246377

Number of patients with heart disease per gender¶

In [24]:
sns.countplot(x="sex", data=heart_data,hue='target')
male, fm = heart_data['sex'].value_counts()

print('Number of Female Patients:', fm)
print('Number of Male Patients:', male)

Number of Female Patients: 96
Number of Male Patients: 207

Median Cholesterol per Gender¶

In [25]:
heart_data_positive_man = heart_data_positive[heart_data_positive['sex'] == 1]
heart_data_positive_woman = heart_data_positive[heart_data_positive['sex'] == 0]

chol_heart_positive_man = heart_data_positive_man['chol'].median()
chol_heart_positive_woman = heart_data_positive_woman['chol'].median()

print(f'The most common cholesterol level for a man with a heart disease is ', chol_heart_positive_man)
print(f'The most common cholesterol level for a woman with a heart disease is ', chol_heart_positive_woman)

labels = ['Man', 'Woman']
median_chol = [chol_heart_positive_man, chol_heart_positive_woman]

plt.figure(figsize=(8, 8))
plt.barh(labels, median_chol, color=['cyan', 'lightpink'])
plt.ylabel('Median Cholesterol')
plt.title('Median Cholesterol per Gender')
plt.xlim(100, 260)

plt.show()

The most common cholesterol level for a man with a heart disease is  228.0
The most common cholesterol level for a woman with a heart disease is  249.0
In [26]:
cp_avg_pos = heart_data_positive['cp'].mean()
cp_avg_neg = heart_data_negative['cp'].mean()

print(f'The average chest pain type of a patient with a heart disease is', cp_avg_pos)
print(f'The average chest pain type of a patient with no heart disease is', cp_avg_neg)

labels = ['Heart Disease', 'No Heart Disease']
average_cp = [cp_avg_pos, cp_avg_neg]

plt.figure(figsize=(8, 8))
plt.bar(labels, average_cp, color=['red', 'lightgreen'])
plt.ylabel('Average Chest Pain Type')
plt.title('Average Chest Pain Type for Patients')

plt.show()

The average chest pain type of a patient with a heart disease is 1.3757575757575757
The average chest pain type of a patient with no heart disease is 0.4782608695652174

Machine Learning Model¶

Setting Feature and Target¶

In [27]:
X = heart_data.drop(columns='target', axis=1)
Y = heart_data['target']
In [28]:
X
Out[28]:
age sex cp trestbps chol fbs restecg thalach exang oldpeak slope ca thal
0 63 1 3 145 233 1 0 150 0 2.3 0 0 1
1 37 1 2 130 250 0 1 187 0 3.5 0 0 2
2 41 0 1 130 204 0 0 172 0 1.4 2 0 2
3 56 1 1 120 236 0 1 178 0 0.8 2 0 2
4 57 0 0 120 354 0 1 163 1 0.6 2 0 2
... ... ... ... ... ... ... ... ... ... ... ... ... ...
298 57 0 0 140 241 0 1 123 1 0.2 1 0 3
299 45 1 3 110 264 0 1 132 0 1.2 1 0 3
300 68 1 0 144 193 1 1 141 0 3.4 1 2 3
301 57 1 0 130 131 0 1 115 1 1.2 1 1 3
302 57 0 1 130 236 0 0 174 0 0.0 1 1 2

303 rows × 13 columns

In [29]:
Y
Out[29]:
0      1
1      1
2      1
3      1
4      1
      ..
298    0
299    0
300    0
301    0
302    0
Name: target, Length: 303, dtype: int64

Setting Data Test and Training¶

In [30]:
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.35, stratify=Y, random_state=2)
In [31]:
X.shape, X_train.shape, X_test.shape
Out[31]:
((303, 13), (196, 13), (107, 13))

Logistic Regression¶

In [32]:
model = LogisticRegression(max_iter=5000)
In [33]:
model.fit(X_train, Y_train)
Out[33]:
LogisticRegression(max_iter=5000)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
LogisticRegression(max_iter=5000)

Accuracy Test¶

In [34]:
X_train_prediction = model.predict(X_train)
training_data_accuracy = accuracy_score(X_train_prediction, Y_train)

print(f'Training data accuracy:', training_data_accuracy)

Training data accuracy: 0.8673469387755102
In [35]:
X_test_prediction = model.predict(X_test)
test_data_accuracy = accuracy_score(X_test_prediction, Y_test)

print(f'Test data accuracy:', test_data_accuracy)

Test data accuracy: 0.8411214953271028
In [36]:
labels = ['Training Data', 'Test Data']
accuracies = [training_data_accuracy, test_data_accuracy]

plt.figure(figsize=(8,8))
plt.bar(labels, accuracies, color=['blue', 'orange'])
plt.ylabel('Accuracy')
plt.title('Training vs Test Data Accuracy')
plt.ylim(0, 1)

plt.show()

Prediction Model¶

In [37]:
input_data = np.array([[63,1,3,145,233,1,0,150,0,2.3,0,0,1]])

feature_names = ['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach', 'exang', 'oldpeak', 'slope', 'ca', 'thal']
input_data_df = pd.DataFrame(input_data, columns=feature_names)

prediction = model.predict(input_data_df)
print(prediction)

if (prediction[0] == 0):
    print('The Person does not have a Heart Disease')
else:
    print('The Person has Heart Disease')

[1]
The Person has Heart Disease
In [38]:
heart_data_positive.head(1)
Out[38]:
age sex cp trestbps chol fbs restecg thalach exang oldpeak slope ca thal target
0 63 1 3 145 233 1 0 150 0 2.3 0 0 1 1
In [39]:
input_data = np.array([[67,1,0,160,286,0,0,108,1,1.5,1,3,2]])

feature_names = ['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach', 'exang', 'oldpeak', 'slope', 'ca', 'thal']
input_data_df = pd.DataFrame(input_data, columns=feature_names)

prediction = model.predict(input_data_df)
print(prediction)

if (prediction[0] == 0):
    print('The Person does not have a Heart Disease')
else:
    print('The Person has Heart Disease')

[0]
The Person does not have a Heart Disease
In [40]:
heart_data_negative.head(1)
Out[40]:
age sex cp trestbps chol fbs restecg thalach exang oldpeak slope ca thal target
165 67 1 0 160 286 0 0 108 1 1.5 1 3 2 0