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Machine Learning Basics for a Newbie
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Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
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Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
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Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
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Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
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Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
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Heart Disease Prediction using Machine learning

VIKRAM
VIKRAM Last Updated : 30 Sep, 2024
6 min read

Overview

Machine Learning is one of the most widely used concepts around the world. It will be essential in the healthcare sectors which will be useful for doctors to fasten the diagnosis. In this article, we will be dealing with the Heart disease dataset and will analyze, predict the result whether the patient has heart disease or normal, i.e. Heart disease prediction using Machine Learning. This prediction will make it faster and more efficient in healthcare sectors which will be a time-consuming process.

This article was published as a part of the Data Science Blogathon.

Table of contents

Importing Libraries

import NumPy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
from sklearn.model_selection import KFold, StratifiedKFold, cross_val_score
from sklearn import linear_model, tree, ensemble

Reading the Data from CSV file

In this section, we will load and view the CSV file and its contents.

Filename: heart.csv

This dataset is sourced from the online repository of UCI.

Link: https://archive.ics.uci.edu/ml/datasets/Heart+Disease

Contents:

We have 14 attributes/features including target which will be age, gender, cholesterol level, exacting, chest pain, old peak, thalach, FBS, slope, thal, etc.

dataframe=pd.read_csv("/content/heart.csv")
data frame.head(10)

Output:

Inference: From the dataset, we have three types of data such as continuous, ordinal, and binary data.

Data Analysis

Let, we get some basic information about the dataset.

dataframe.info()

Output:

Data Analysis

Now, let us look at whether the dataset has null values or not.

dataframe.isna().sum()

Output:

Data Analysis

Inference: From this output, our data does not contain null values and duplicates. So, the data is good which will be further analyzed.

Correlation Matrix

Visulaizing the data features to find the correlation between them which will infer the important features.

plt.figure(figsize=(15,10))
sns.heatmap(dataframe.corr(),linewidth=.01,annot=True,cmap="winter")
plt.show()
plt.savefig('correlationfigure')

Output:

heatmap

Inference:

From the above heatmap, we can understand that Chest pain(cp) and target have a positive correlation. It means that whose has a large risk of chest pain results in a greater chance to have heart disease. In addition to chest pain, thalach, slope, and resting have a positive correlation with the target.

Then, exercise-induced angina(exang) and the target have a negative correlation which means when we exercise, the heart requires more blood, but narrowed arteries slow down the blood flow. In addition to ca, old peak, thal have a negative correlation with the target.

Let us see the relation between each features distribution with the help of histogram.

dataframe.hist(figsize=(12,12))
plt.savefig('featuresplot')

Output:

histogram

Train-Test Split

X_train, X_test,y_train, y_test=train_test_split(X,y,test_size=0.25,random_state=40)

We split the whole dataset into trainset and testset which contains 75% train and 25% test.

We can include this train set into classifiers to train our model and the test set is useful for predicting the performance of the model by different classifiers.

Algorithm Implementation

1 .Logistic Regression

from sklearn.model_selection import cross_val_score, GridSearchCV
from sklearn.linear_model import LogisticRegression
lr=LogisticRegression(C=1.0, class_weight='balanced', dual=False,
                   fit_intercept=True, intercept_scaling=1, l1_ratio=None,
                   max_iter=100, multi_class='auto', n_jobs=None, penalty='l2',
                   random_state=1234, solver='lbfgs', tol=0.0001, verbose=0,
                   warm_start=False)
model1=lr.fit(X_train,y_train)
prediction1=model1.predict(X_test)
from sklearn.metrics import confusion_matrix
cm=confusion_matrix(y_test,prediction1)
cm
sns.heatmap(cm, annot=True,cmap='winter',linewidths=0.3, linecolor='black',annot_kws={"size": 20})
TP=cm[0][0]
TN=cm[1][1]
FN=cm[1][0]
FP=cm[0][1]

print('Testing Accuracy for Logistic Regression:',(TP+TN)/(TP+TN+FN+FP))
print('Testing Sensitivity for Logistic Regression:',(TP/(TP+FN)))
print('Testing Specificity for Logistic Regression:',(TN/(TN+FP)))
print('Testing Precision for Logistic Regression:',(TP/(TP+FP)))

Output:

Heart Disease prediction using Machine Learning
from sklearn.metrics import classification_report
print(classification_report(y_test, prediction1))
Dataset

Inference: From the above report, we get the accuracy of the Logistic Regression classifier is about 80%.

2. Decision Tree

from sklearn.model_selection import RandomizedSearchCV
from sklearn.tree import DecisionTreeClassifier

tree_model = DecisionTreeClassifier(max_depth=5,criterion='entropy')
cv_scores = cross_val_score(tree_model, X, y, cv=10, scoring='accuracy')
m=tree_model.fit(X, y)
prediction=m.predict(X_test)
cm= confusion_matrix(y_test,prediction)
sns.heatmap(cm, annot=True,cmap='winter',linewidths=0.3, linecolor='black',annot_kws={"size": 20})
print(classification_report(y_test, prediction))

TP=cm[0][0]
TN=cm[1][1]
FN=cm[1][0]
FP=cm[0][1]
print('Testing Accuracy for Decision Tree:',(TP+TN)/(TP+TN+FN+FP))
print('Testing Sensitivity for Decision Tree:',(TP/(TP+FN)))
print('Testing Specificity for Decision Tree:',(TN/(TN+FP)))
print('Testing Precision for Decision Tree:',(TP/(TP+FP)))

Output:

Heart Disease prediction using Machine Learning

 

Inference: From the above report, we get the accuracy of the Decision Tree classifier is about 92%.

3. Random Forest Classifier

from sklearn.ensemble import RandomForestClassifier
rfc=RandomForestClassifier(n_estimators=500,criterion='entropy',max_depth=8,min_samples_split=5)
model3 = rfc.fit(X_train, y_train)
prediction3 = model3.predict(X_test)
cm3=confusion_matrix(y_test, prediction3)
sns.heatmap(cm3, annot=True,cmap='winter',linewidths=0.3, linecolor='black',annot_kws={"size": 20})
TP=cm3[0][0]
TN=cm3[1][1]
FN=cm3[1][0]
FP=cm3[0][1]
print(round(accuracy_score(prediction3,y_test)*100,2))
print('Testing Accuracy for Random Forest:',(TP+TN)/(TP+TN+FN+FP))
print('Testing Sensitivity for Random Forest:',(TP/(TP+FN)))
print('Testing Specificity for Random Forest:',(TN/(TN+FP)))
print('Testing Precision for Random Forest:',(TP/(TP+FP)))

Output:

Heart Disease prediction using Machine Learning

Let us see the classification report for Random Forest Classifier:

print(classification_report(y_test, prediction3))

Output:

Heart Disease prediction using Machine Learning

Inference: From the above report, we can get the accuracy of the Random Forest classifier is about 80%.

4. Support Vector Machines(SVM)

  from sklearn.svm import SVC
  svm=SVC(C=12,kernel='linear')
  model4=svm.fit(X_train,y_train)
  prediction4=model4.predict(X_test)
  cm4= confusion_matrix(y_test,prediction4)
  sns.heatmap(cm4, annot=True,cmap='winter',linewidths=0.3, linecolor='black',annot_kws={"size": 20})
  TP=cm4[0][0]
  TN=cm4[1][1]
  FN=cm4[1][0]
  FP=cm4[0][1]
  
  print('Testing Accuracy for SVM:',(TP+TN)/(TP+TN+FN+FP))
  print('Testing Sensitivity for Random Forest:',(TP/(TP+FN)))
  print('Testing Specificity for Random Forest:',(TN/(TN+FP)))
  print('Testing Precision for Random Forest:',(TP/(TP+FP)))

Output:

Heart Disease prediction using Machine Learning

Let us see the classification report of SVM:

print(classification_report(y_test, prediction4))

Output:

Output

Inference: From the above report, we get the accuracy of the Support Vector Machine classifier is about 82%.

From the results that we got, as four machine learning algorithms like Logistic Regression, Random Forest, Support Vector Machines and Decision Trees. From the final results, we got Logistic Regression as 80%, Random Forest as 80%, Support Vector Machines as 82%, and Decision Trees as 92%. We can conclude that the Decision Tree algorithm is the best algorithm for our model with the highest accuracy around 92 percent.

Final Model Implementation

Now, we can apply the best working algorithm (i.e., Decision Tree Classifier) into our model and check whether our model will result in the correct output or not with the help of available data.

CASE 1 – For Heart Disease data

input=(63,3,145,233,150,2.3,0)
input_as_numpy=np.asarray(input)
input_reshaped=input_as_numpy.reshape(1,-1)
pre1=tree_model.predict(input_reshaped)
if(pre1==1): 
  print("The patient seems to be have heart disease:(")
else:
  print("The patient seems to be Normal:)")

Output:

CASE 2 – For Normal Data

input=(72,1,125,200,150,1.3,1)
input_as_numpy=np.asarray(input)
input_reshaped=input_as_numpy.reshape(1,-1)
pre1=tree_model.predict(input_reshaped)
if(pre1==1): 
  print("The patient seems to be have heart disease:(")
else:
  print("The patient seems to be Normal:)")

Output:

Conclusion

Finally, we can conclude that real-time predictors will be essential in the healthcare sector nowadays. From this project, we will be able to predict real-time heart disease using the patient’s data from the model using the Decision Tree Algorithm, thereby making accurate heart disease prediction using machine learning. I hope that you are all excited about the blog. Let us know your thoughts in the comments!

Key Takeaways

  • This process involves data cleaning, data statistics, getting insights from the dataset.
  • This involves four machine learning algorithms which will result in performance metrics of the model.
  • The well-doing algorithm is implemented in the model and checking results with the real-time data.

Frequently Asked Questions

Q1. What is the role of machine learning in heart disease?

A. Machine learning plays a crucial role in heart disease by enabling early detection, accurate diagnosis, and personalized treatment. It can analyze large amounts of patient data, including medical records, imaging tests, and genetic information, to identify patterns and predict the risk of developing heart disease. Machine learning algorithms can also assist in identifying specific heart conditions, such as arrhythmias, based on ECG data. Moreover, they aid in developing personalized treatment plans by considering individual patient characteristics and response to therapies. By leveraging machine learning, healthcare professionals can improve patient outcomes, optimize resource allocation, and enhance overall cardiac care.

Q2. What algorithm is used for heart disease prediction?

A. There are several machine learning algorithms used for heart disease prediction, including logistic regression, decision trees, random forests, support vector machines (SVM), and artificial neural networks. Each algorithm has its strengths and limitations, and the choice depends on the specific dataset and problem at hand. Ensemble methods, such as random forests, are often favoured for their ability to combine multiple algorithms and improve prediction accuracy.

Q3. how machine learning is used in heart disease prediction?

Machine learning algorithms, fed with vast patient data, effectively predict heart disease risk, enabling personalized medicine and improved healthcare outcomes.

The media shown in this article is not owned by Analytics Vidhya and are used at the Author’s discretion.

 

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VIKRAM
VIKRAM

I am a final year student pursuing Electronics and Communication Engineering at Sri Krishna College of Engineering and Technology, Coimbatore. I am always enthusiastic to learn new things and solve new challenges.
I am interested in Machine Learning and also did projects related to supervised learning. Also, I have done learning stuffs on Numpy, Pandas, Matplotlib, ML algos and so on.
I am skilled in C, Java, Data Structures, Algorithms, Python and Problem Solving.
I also wrote blogs on Machine Learning and Data Analysis which can see in "Medium.com".
I have started a YouTube Channel in August 2020, which has now more than 200K+ views and have more than 150+ videos. It is based on smartphones comparison and trolls.

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