Heart Disease Prediction System

This project implements a comprehensive web-based system to predict the risk of heart disease using a machine learning model.

Try it out at heart-disease-prediction.harshbanka.codes

NOTE: Server start may take a minute or two.

Features

• User Input Form: An intuitive web form for users to enter their health data.
• Machine Learning Model: A trained model that predicts heart disease risk based on the provided input.
• Prediction Display: Clear presentation of the prediction result to the user.
• MongoDB Integration: Stores user input and prediction results in a MongoDB database.
• Prediction History: A history page to view past prediction records.

Technologies

• Python
• FastAPI
• Jinja2 Templates
• MongoDB
• pymongo
• HTML, CSS, Bootstrap
• JavaScript

Setup Instructions

1. Clone the Repository:

   git clone https://github.com/<your-username>/<project-repository-name>

2. Create a Virtual Environment:

   • Recommended: Use a tool like venv or conda.
   • Example (venv):

   python3 -m venv env
   source env/bin/activate 

3. Install Dependencies:

   pip install -r requirements.txt

4. MongoDB Setup

   • Local Installation:
     • Follow the instructions from the official MongoDB website: https://www.mongodb.com/docs/
     • Start the MongoDB server (usually with the mongod command).
   • Cloud Service (MongoDB Atlas):
     • Create a MongoDB Atlas account: https://www.mongodb.com/cloud/atlas
     • Set up a cluster and obtain your database connection string.

5. Configure MongoDB Connection

   • Option 1: Export MONGO_URL in Terminal

     • Set the MONGO_URL environment variable in your terminal before running the application:

       export MONGO_URL="your_mongo_connection_string"

   • Option 2: Update Manually in the File
     • If you prefer not to use environment variables, you can manually update the MONGO_URL variable in the file with your MongoDB connection string.
     • Locate utils/database.py or a similar file where you manage database connections.
     • Update the connection function (e.g., connect_to_mongo()) with your MongoDB connection details:**

     # ... other code ...
           MONGO_URL = os.environ.get('MONGO_URL') or "mongodb://127.0.0.1:27017/"
           client = pymongo.MongoClient(MONGO_URL)    # Replace with your connection string 
         # ... rest of the code ... 

6. Run the Application:

uvicorn app:app --reload 

Accessing the System

Open http://127.0.0.1:8000 in your web browser to use the heart disease prediction system.

Model Training

The machine learning model used for heart disease prediction is SVM using scikit-learn.

License

This project is licensed under the MIT License. For more details, see the LICENSE file.