Heart Disease Prediction: Machine Learning

This machine learning exercise is to predict whether a person is healthy or at risk of heart disease. It starts with collecting heart-related data such as age, blood pressure, and cholesterol levels. After cleaning and preparing the data through preprocessing, it’s split into training and testing sets. A logistic regression model is then trained to recognize patterns in the data. Once trained, the model can take new patient information and accurately predict if the individual is likely to be healthy or may have heart disease — helping enable early warning and better health decisions.

Work Flow : Heart Data >> Data Pre processing >> Train Test Split>> Logistic Regression

New Data >> Trained Logistic Regression >> Healthy or heart Disease

Importing the dependencies:

• import numpy as np
• import pandas as pd
• from sklearn.model_selection import train_test_split
• from sklearn.linear_model import LogisticRegression
• from sklearn.metrics import accuracy_score

Data Collection and Processing:

• # loading the csv data to a Pandas dataframe
• heart_dataset =pd.read_csv(‘/content/heart (1).csv’)

link to practice the dataset :

https://www.kaggle.com/datasets/johnsmith88/heart-disease-dataset

• # print first 5 rows of the dataset
• heart_dataset.head()

• # print last 5 rows of the dataset
• heart_dataset.tail()

• # find the shape of the datset
• heart_dataset.shape

• # find the info of the dataset
• heart_dataset.info()

• #checking for missing values
• heart_dataset.isnull().sum()

• # statistical measures about the data
• heart_dataset.describe()

• # checking the distribution of Target Variable
• heart_dataset[‘target’].value_counts()

• 1 –> Defective heart
• 0 –> Healthy heart

Splitting the features and the Target

• x= heart_dataset.drop(columns = ‘target’, axis = 1)
• y= heart_dataset[‘target’]
• print(x)
• print(y)

Splitting the dataset into Training and Testing data

• x_train, x_test,y_train,y_test = train_test_split(x,y,test_size= 0.2, stratify = y, random_state=2)
• print(x.shape, x_train.shape, x_test.shape)

Model Training: Logistic Regression

• model = LogisticRegression()
• # training the LogisticRegression model with Training Data
• model.fit(x_train, y_train)

Model Evaluation: Accuracy Score

• # accuracy on training data
• x_train_prediction= model.predict(x_train)
• training_data_accuracy = accuracy_score(x_train_prediction, y_train)
• print(training_data_accuracy)

• # accuracy on test data
• x_test_prediction = model.predict(x_test)
• test_data_accuracy = accuracy_score(x_test_prediction, y_test)
• print(test_data_accuracy)

Building a Predictive System

• input_data=(62,0,0,140,268,0,0,160,0,3.6,0,2,1)

• # 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 on instance
• input_data_reshaped = input_data_as_numpy_array.reshape(1,-1)
• prediction = model.predict(input_data_reshaped)
• print(prediction)
• if (prediction[0]==0):
• print(‘The person does not have a heart disease’)
• else:
• print(‘The person has a heart disease’)

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