HEART WISE

Heart Disease Prediction

This project aims to predict the presence of heart disease in patients based on various factors using a machine learning model. The dataset used in this project contains information about patients, including their age, sex, chest pain type, blood pressure, cholesterol level, fasting blood sugar, electrocardiography results, exercise-induced angina, ST depression, slope of peak exercise ST segment, number of major vessels, and thalassemia. The target variable is the presence or absence of heart disease.

Project Structure

The project consists of the following files:

model.py: Contains the code for training a K-nearest neighbors (KNN) classifier on the heart disease dataset.
app.py: Implements a Flask application to create a web interface for predicting heart disease using the trained model.
accuracy.py: Calculates and prints the accuracy of the KNN classifier for different values of n_neighbors.
Prediction.py: Demonstrates how to use the trained model for predicting heart disease for specific input values.
templates/main.html: HTML template for the main page of the web application.
templates/result.html: HTML template for displaying the prediction result.
static/style.css: CSS file for styling the web application.
heart.csv: The heart disease dataset in CSV format.
requirements.txt:List of all required files to run the project.
README.md: This file.

Usage

Install the necessary dependencies by running pip install -r requirements.txt.
Train the model by running python model.py. This will generate a file named model.pkl.
Start the Flask application by running python app.py.
Access the web interface by visiting http://localhost:5000 in your browser.
Enter the required information and click the "Predict" button to get the prediction result.

Screenshots

Main Page

Result Page

Contributors

Feel free to contribute by submitting bug reports, feature requests, or pull requests on GitHub.

License

This project is licensed under the MIT License.

Raviruler/HeartWise