Skip to content
Related Articles

Related Articles

ML | Heart Disease Prediction Using Logistic Regression .
  • Last Updated : 26 Mar, 2020

World Health Organization has estimated that four out of five cardiovascular diseases(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 techniques classifying records of a dataset based on the values of the input fields . It predicts a dependent variable based on one or more set of independent variables to predict outcomes . It can be used both for binary classification and multi-class classification. To know more about it, click here .

Code: Loading the 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 sn

Data Preparation :
The dataset is publically available on the Kaggle website, and it 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-years 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("../input / framingham.csv")
disease_df.drop(['education'], inplace = True, axis = 1)
disease_df.rename(columns ={'male':'Sex_male'}, inplace = True)
  
# 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  ...  heartRate  glucose  TenYearCHD
0         1   39              0  ...       80.0     77.0           0
1         0   46              0  ...       95.0     76.0           0
2         1   48              1  ...       75.0     70.0           0
3         0   61              1  ...       65.0    103.0           1
4         0   46              1  ...       85.0     85.0           0

[5 rows x 15 columns] (3751, 15)
0    3179
1     572
Name: TenYearCHD, dtype: int64

Code: 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))
sn.countplot(x ='TenYearCHD', data = disease_df, 
             palette ="BuGn_r" )
plt.show()

Output : Graph Display :

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




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

Output: Graph Display :

Code : Training and Test Sets: Splitting Data | Normalization of the Dataset




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

Code: Modeling of the Dataset | Evaluation and Accuracy :




from sklearn.linear_model import LogisticRegression
logreg = LogisticRegression()
logreg.fit(X_train, y_train)
y_pred = logreg.predict(X_test)
  
# Evaluation and accuracy
from sklearn.metrics import jaccard_similarity_score
print('')
print('Accuracy of the model in jaccard similarity score is = '
      jaccard_similarity_score(y_test, y_pred))

Output :

Accuracy of the model in jaccard similarity score is = 0.8490230905861457

Code: Using Confusion Matrix to find the Acuuracy of the model :




# 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))
sn.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 contributed by parna_28 .

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

Confusion Matrix :

machine-learning

My Personal Notes arrow_drop_up
Recommended Articles
Page :