Heart Disease Prediction using Support Vector Machine

Heart disease is a significant health concern worldwide, and early detection plays a crucial role in effective treatment and prevention. Machine learning algorithms, such as Support Vector Machines (SVM), have shown promising results in predicting heart disease based on patient data.

SVM stands for Support Vector Machine are a set of supervised learning methods used for classification, regression and outliers detection. SVM can be imagined as a surface that maximizes the boundaries between various types of points of data that is represented in multi-dimensional space also known as hyperplane. It can be used for both binary classification and multi-class classification

Implementation of Heart Disease Prediction using Support Vector Machine

Here, we will use the heart disease dataset. The dataset provides the information of age, sex, cp, trestbps and other terms based on which the candidate is labeled as 0 and 1.

Importing Necessary Libraries

Python3

import numpy as np
import pandas as pd
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split

Loading the dataset

Python3

heart_data = pd.read_csv('heart.csv')
heart_data.info()

Output:

RangeIndex: 1025 entries, 0 to 1024
Data columns (total 14 columns):
 #   Column    Non-Null Count  Dtype  
---  ------    --------------  -----  
 0   age       1025 non-null   int64  
 1   sex       1025 non-null   int64  
 2   cp        1025 non-null   int64  
 3   trestbps  1025 non-null   int64  
 4   chol      1025 non-null   int64  
 5   fbs       1025 non-null   int64  
 6   restecg   1025 non-null   int64  
 7   thalach   1025 non-null   int64  
 8   exang     1025 non-null   int64  
 9   oldpeak   1025 non-null   float64
 10  slope     1025 non-null   int64  
 11  ca        1025 non-null   int64  
 12  thal      1025 non-null   int64  
 13  target    1025 non-null   int64  
dtypes: float64(1), int64(13)
memory usage: 112.2 KB

Now after loading the dataset we observe the target attribute present in the dataset .

Selecting the Target Variable

Python3

X = heart_data.drop(columns='target',axis=1)
Y = heart_data['target']
heart_data['target'].value_counts()

Output:

1    526
0    499
Name: target, dtype: int64

Splitting the data

Python3

X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, stratify=Y, random_state=2)

Fitting the Model

• we have imported SVC model from sklearn.svm module and the object name of our model is clf1 and in the next line the model is trained successfully .
• In SVC we have used kernel function to convert low dimensional feature space to high dimensional feature space to make the data present in dataset classify properly

Python3

from sklearn.svm import SVC
clf1 = SVC(kernel="linear")
clf1.fit(X_train,Y_train)

Output :

SVC
SVC(kernel='linear') .

Testing Accuracy of the model

Python3

y_pred= clf1.predict(X_test)
from sklearn.metrics import classification_report

# Evaluate the classification report
classification_rep = classification_report(y_pred,Y_test)
print("Classification Report:")
print(classification_rep)

Output:

Classification Report:
              precision    recall  f1-score   support
           0       0.72      0.90      0.80        80
           1       0.92      0.78      0.84       125
    accuracy                           0.82       205
   macro avg       0.82      0.84      0.82       205
weighted avg       0.84      0.82      0.83       205

Defining a Prediction Function

1. Function Definition: The function predict_heart_disease takes two arguments: model (the trained SVM model) and input_data (a tuple containing input features for a single instance).
2. Convert Input Data to NumPy Array: The input data is converted to a NumPy array using np.asarray(input_data). This ensures that the input data is in a format compatible with the SVM model.
3. Reshape Input Data: The NumPy array is reshaped to match the expected input shape of the SVM model. In this case, the shape is (1, -1), where -1 indicates that NumPy should infer the number of columns based on the number of elements in the input data.
4. Make Prediction: The SVM model’s predict method is used to make a prediction on the reshaped input data. The result is stored in the prediction variable.
5. Print Prediction Result: Based on the prediction result, the function prints either “The Person does not have Heart Disease” if prediction[0] is 0, or “The Person has Heart Disease” if prediction[0] is not 0.

Python3

def predict_heart_disease(model, input_data):
    # Change the input data to a numpy array
    input_data_as_numpy_array = np.asarray(input_data)

    # Reshape the numpy array as we are predicting for only one instance
    input_data_reshaped = input_data_as_numpy_array.reshape(1, -1)

    # Make the prediction
    prediction = model.predict(input_data_reshaped)

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

Prediction on Random Data

Python3

input_data = (62, 0, 0, 140, 268, 0, 0, 160, 0, 3.6, 0, 2, 2)
predict_heart_disease(clf1, input_data)

Output:

The Person does not have Heart Disease