Patient Readmission Prediction

This project predicts patient readmissions using machine learning. It includes a Flask API for making predictions and a Streamlit dashboard for interactive visualization.

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DEMO!

Project Demo

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Table of Contents

1. Project Overview
2. Setup Instructions
3. File Descriptions
4. Example Usage
5. Contributing

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Project Overview

The goal of this project is to predict whether a patient will be readmitted to the hospital based on their medical data. The project includes:

• A machine learning model trained on patient data.
• A Flask API to serve predictions.
• A Streamlit dashboard for interactive predictions and visualization.

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Setup Instructions

Step 1: Clone the Repository

Clone the repository and navigate to the project directory:

git clone https://github.com/your-username/PatientReadmissionPrediction.git
cd PatientReadmissionPrediction

Step 2: Install Dependencies

Create a virtual environment (optional but recommended):

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python -m venv venv
source venv/bin/activate  # For Linux/Mac
venv\Scripts\activate     # For Windows

Install the required libraries:

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pip install -r requirements.txt

Step 3: Download the Dataset

Download the dataset from the UCI Repository or Kaggle. Rename the dataset to diabetes_data.csv. Place it in the data/ folder.

Step 4: Train the Model

Run the script to preprocess the data, train the model, and save it:

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python app/model.py

Step 5: Start the Flask API

Run the Flask app to serve predictions:

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python app/web_service.py

Step 6: Launch the Streamlit Dashboard

Run the Streamlit app for interactive predictions:

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streamlit run dashboard/streamlit_app.py

File Descriptions

Project Structure

PatientReadmissionPrediction/
├── data/
│   └── diabetes_data.csv          # Dataset for training and testing
├── app/
│   ├── __init__.py                # Empty file for package initialization
│   ├── model.py                   # Code for preprocessing, training, and saving the model
│   └── web_service.py             # Flask API for serving predictions
├── dashboard/
│   └── streamlit_app.py           # Streamlit dashboard for interactive predictions
├── requirements.txt               # List of dependencies
└── README.md                      # Project documentation

Contributing

Contributions are welcome! Follow these steps to contribute:

Fork the repository.

Create a new branch:

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git checkout -b feature/your-feature-name

Commit your changes:

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git commit -m "Add your message here"

Push to the branch:

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git push origin feature/your-feature-name

Open a pull request.

Contact

For questions or feedback, please contact:

Your Name: rsuhas319@gmail.com

GitHub: suhas-ramesha

Built With

• python