World Health Organization has estimated that four out of five cardiovascular disease (CVD) deaths are due to heart attacks. This whole research intends to pinpoint the ratio of patients who possess a good chance of being affected by CVD and also to predict the overall risk using Logistic Regression.

What is Logistic Regression?

Logistic Regression is a statistical and machine-learning technique classifying records of a dataset based on the values of the input fields. It predicts a dependent variable based on one or more sets of independent variables to predict outcomes. It can be used both for binary classification and multi-class classification.  

Heart Disease Prediction Using Logistic Regression

Importing Necessary Libraries

import pandas as pd
import pylab as pl
import numpy as np
import scipy.optimize as opt
import statsmodels.api as sm
from sklearn import preprocessing
'exec(% matplotlib inline)'
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab
import seaborn as sns

Data Preparation

The dataset is from an ongoing cardiovascular study on residents of the town of Framingham, Massachusetts. The classification goal is to predict whether the patient has 10-year risk of future coronary heart disease (CHD). The dataset provides the patients’ information. It includes over 4,000 records and 15 attributes.

Loading the Dataset

# dataset
disease_df = pd.read_csv("framingham.csv")
disease_df.drop(['education'], inplace = True, axis = 1)
disease_df.rename(columns ={'male':'Sex_male'}, inplace = True)

Handling Missing Values

# removing NaN / NULL values
disease_df.dropna(axis = 0, inplace = True)
print(disease_df.head(), disease_df.shape)
print(disease_df.TenYearCHD.value_counts())

Output:

   Sex_male  age  currentSmoker  cigsPerDay  BPMeds  prevalentStroke  
0         1   39              0         0.0     0.0                0   
1         0   46              0         0.0     0.0                0   
2         1   48              1        20.0     0.0                0   
3         0   61              1        30.0     0.0                0   
4         0   46              1        23.0     0.0                0   
   prevalentHyp  diabetes  totChol  sysBP  diaBP    BMI  heartRate  glucose  
0             0         0    195.0  106.0   70.0  26.97       80.0     77.0   
1             0         0    250.0  121.0   81.0  28.73       95.0     76.0   
2             0         0    245.0  127.5   80.0  25.34       75.0     70.0   
3             1         0    225.0  150.0   95.0  28.58       65.0    103.0   
4             0         0    285.0  130.0   84.0  23.10       85.0     85.0   
   TenYearCHD  
0           0  
1           0  
2           0  
3           1  
4           0   (3751, 15)
0    3179
1     572
Name: TenYearCHD, dtype: int64

Splitting the Dataset into Test and Train Sets

X = np.asarray(disease_df[['age', 'Sex_male', 'cigsPerDay', 
                           'totChol', 'sysBP', 'glucose']])
y = np.asarray(disease_df['TenYearCHD'])

# normalization of the dataset
X = preprocessing.StandardScaler().fit(X).transform(X)

# Train-and-Test -Split
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split( 
        X, y, test_size = 0.3, random_state = 4)

print ('Train set:', X_train.shape,  y_train.shape)
print ('Test set:', X_test.shape,  y_test.shape)

Output:

Train set: (2625, 6) (2625,)
Test set: (1126, 6) (1126,)

Exploratory Data Analysis of Heart Disease Dataset

Ten Year’s CHD Record of all the patients available in the dataset:

# counting no. of patients affected with CHD
plt.figure(figsize=(7, 5))
sns.countplot(x='TenYearCHD', data=disease_df,
             palette="BuGn_r")
plt.show()

# This code is modified by Susobhan Akhuli

Output:

Counting number of patients affected by CHD where (0= Not Affected; 1= Affected)

laste = disease_df['TenYearCHD'].plot()
plt.show(laste)

Output:

Fitting Logistic Regression Model for Heart Disease Prediction

from sklearn.linear_model import LogisticRegression
logreg = LogisticRegression()
logreg.fit(X_train, y_train)
y_pred = logreg.predict(X_test)

Evaluating Logistic Regression Model

# Evaluation and accuracy
from sklearn.metrics import accuracy_score
print('Accuracy of the model is =', 
      accuracy_score(y_test, y_pred))

Output:

Accuracy of the model is = 0.8490230905861457

Confusion Matrix

# Confusion matrix 
from sklearn.metrics import confusion_matrix, classification_report

cm = confusion_matrix(y_test, y_pred)
conf_matrix = pd.DataFrame(data = cm, 
                           columns = ['Predicted:0', 'Predicted:1'], 
                           index =['Actual:0', 'Actual:1'])

plt.figure(figsize = (8, 5))
sns.heatmap(conf_matrix, annot = True, fmt = 'd', cmap = "Greens")

plt.show()
print('The details for confusion matrix is =')
print (classification_report(y_test, y_pred))

# This code is modified by Susobhan Akhuli

Output:

The details for confusion matrix is =
              precision    recall  f1-score   support
           0       0.85      0.99      0.92       951
           1       0.61      0.08      0.14       175
    accuracy                           0.85      1126
   macro avg       0.73      0.54      0.53      1126
weighted avg       0.82      0.85      0.80      1126

Get complete notebook link here :

Notebook: click here.

Dataset: click here.