Heart Disease Prediction API

This repository contains a FastAPI-based API for predicting heart disease risk using machine learning.

Prerequisites

• Python 3.10 or higher
• pip (Python package manager)
• Git

Installation

1. Clone the repository:

   git clone https://github.com/seanthw/heart_disease_prediction_app_lablab.git
   cd heart_disease_prediction
   

2. Create a virtual environment:

   python -m venv venv
   

3. Activate the virtual environment:

   • On Windows:

     venv\Scripts\activate
     

   • On macOS and Linux:

     source venv/bin/activate
     

4. Install the required packages:

   pip install -r requirements.txt
   

Configuration

1. Ensure the heart_disease_model.pkl file is in the backend/models/ directory.

2. Set up the database:

   • The application uses SQLite by default. The database file will be created automatically when you run the application for the first time.
   • If you want to use a different database, update the DATABASE_URL in backend/app/database.py.

3. (Optional) Set environment variables:

   • SECRET_KEY: Used for token encryption. If not set, a default value will be used.
   • ALGORITHM: JWT algorithm. Default is HS256.
   • ACCESS_TOKEN_EXPIRE_MINUTES: Token expiration time. Default is 30 minutes.

Running the Application

1. Navigate to the backend directory:

   cd backend
   

2. Start the FastAPI server:

   uvicorn app.main:app --host 0.0.0.0 --port 8000
   

3. The API will be available at http://localhost:8000.

API Endpoints and Examples

Register a New User

Endpoint: POST /register

Example Request:

curl -X POST "http://localhost:8000/register" \
     -H "Content-Type: application/json" \
     -d '{"email": "[email protected]", "password": "strongpassword123"}'

Example Response:

{
  "email": "[email protected]",
  "id": 1
}

Obtain an Access Token

Endpoint: POST /token

Example Request:

curl -X POST "http://localhost:8000/token" \
     -H "Content-Type: application/x-www-form-urlencoded" \
     -d "[email protected]&password=strongpassword123"

Example Response:

{
  "access_token": "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9...",
  "token_type": "bearer"
}

Make a Prediction

Endpoint: POST /predict

Example Request:

curl -X POST "http://localhost:8000/predict" \
     -H "Authorization: Bearer YOUR_ACCESS_TOKEN" \
     -H "Content-Type: application/json" \
     -d '{
       "age": 50,
       "sex": 1,
       "cp": 0,
       "trestbps": 140,
       "chol": 233,
       "fbs": 1,
       "restecg": 0,
       "thalach": 150,
       "exang": 0,
       "oldpeak": 2.3,
       "slope": 0,
       "ca": 0,
       "thal": 1
     }'

Example Response:

{
  "prediction": 1,
  "probability": 0.75,
  "risk_category": "High",
  "message": "This prediction suggests a higher likelihood of heart disease. Please consult with a healthcare professional for proper evaluation and advice."
}

Retrieve Prediction History

Endpoint: GET /data

Example Request:

curl -X GET "http://localhost:8000/data" \
     -H "Authorization: Bearer YOUR_ACCESS_TOKEN"

Example Response:

[
  {
    "id": 1,
    "age": 50,
    "sex": 1,
    "cp": 0,
    "trestbps": 140,
    "chol": 233,
    "fbs": 1,
    "restecg": 0,
    "thalach": 150,
    "exang": 0,
    "oldpeak": 2.3,
    "slope": 0,
    "ca": 0,
    "thal": 1,
    "target": 1,
    "user_id": 1
  }
]

Dummy Data for Testing

You can use the following dummy data to test the prediction endpoint:

1. High Risk Case:

   {
     "age": 65,
     "sex": 1,
     "cp": 2,
     "trestbps": 160,
     "chol": 280,
     "fbs": 1,
     "restecg": 2,
     "thalach": 130,
     "exang": 1,
     "oldpeak": 3.1,
     "slope": 2,
     "ca": 2,
     "thal": 3
   }

2. Low Risk Case:

   {
     "age": 35,
     "sex": 0,
     "cp": 0,
     "trestbps": 120,
     "chol": 180,
     "fbs": 0,
     "restecg": 0,
     "thalach": 170,
     "exang": 0,
     "oldpeak": 0.5,
     "slope": 0,
     "ca": 0,
     "thal": 1
   }

3. Medium Risk Case:

   {
     "age": 50,
     "sex": 1,
     "cp": 1,
     "trestbps": 140,
     "chol": 230,
     "fbs": 1,
     "restecg": 1,
     "thalach": 150,
     "exang": 0,
     "oldpeak": 1.5,
     "slope": 1,
     "ca": 1,
     "thal": 2
   }

Understanding the Input Features

• age: Age in years
• sex: Sex (1 = male; 0 = female)
• cp: Chest pain type (0 = typical angina; 1 = atypical angina; 2 = non-anginal pain; 3 = asymptomatic)
• trestbps: Resting blood pressure (in mm Hg on admission to the hospital)
• chol: Serum cholesterol in mg/dl
• fbs: Fasting blood sugar > 120 mg/dl (1 = true; 0 = false)
• restecg: Resting electrocardiographic results (0 = normal; 1 = having ST-T wave abnormality; 2 = showing probable or definite left ventricular hypertrophy)
• thalach: Maximum heart rate achieved
• exang: Exercise induced angina (1 = yes; 0 = no)
• oldpeak: ST depression induced by exercise relative to rest
• slope: The slope of the peak exercise ST segment (0 = upsloping; 1 = flat; 2 = downsloping)
• ca: Number of major vessels (0-3) colored by fluoroscopy
• thal: 1 = normal; 2 = fixed defect; 3 = reversable defect

API Documentation

FastAPI provides automatic interactive API documentation. Once the server is running, you can access:

• Swagger UI: http://localhost:8000/docs
• ReDoc: http://localhost:8000/redoc

Testing

To run the tests:

pytest

License

[Include your chosen license here]

Contributing

[Include guidelines for contributing to your project]

Support

If you encounter any problems or have any questions, please open an issue in the GitHub repository.