Acute-Inflammation-Prediction

Federated Learning for Acute Inflammation Prediction

• This project demonstrates federated learning for predicting acute inflammation using a neural network model. The dataset used for training and testing contains information about patients' symptoms and diagnoses. The goal is to predict two diseases: inflammation of the urinary bladder and nephritis of renal pelvis origin.
  

Requirements

• Python 3.x
  
• PyTorch
  
• pandas
  
• scikit-learn
  
• ipywidgets
  

Installation

• Clone the repository:
  

• git clone https://github.com/saikrishy3808u3qr3pur3q/Acute-Inflammation-Prediction
  

• cd your_repository
  

• Install the required packages:
  

• pip install -r requirements.txt
  

Usage

• Prepare the dataset: Download the dataset from here and place it in the project directory.
  

• Run the Jupyter notebook federated_learning_acute_inflammation.ipynb to train the model using federated learning and evaluate its performance.
  

• Follow the instructions in the notebook to interact with the UI for making predictions on new data.
  

Files

• federated_learning_acute_inflammation.ipynb: Jupyter notebook containing the code for federated learning.
  
• diagnosis_1.csv: Dataset containing patient information.
  

Dataset

• https://archive.ics.uci.edu/dataset/184/acute+inflammations

Acknowledgements

• Special thanks to Sathishkumar for their contributions and support.

License

This project is licensed under the MIT License - see the LICENSE.md file for details.