This GitHub repository hosts the codebase related to Haohui Lu's thesis, titled "Chronic Disease Prediction Using Graph Machine Learning". The work focuses on exploring advanced machine learning methodologies, specifically graph-based learning techniques, for predicting chronic diseases.
The repository is organised into different folders, each corresponding to a specific chapter in the thesis. The structure ensures ease of navigation and better understanding of the content.
Each chapter's folder contains:
code: Python scripts and Jupyter notebooks showcasing the implementations discussed in the chapter.data: Sample data sets or links to the data sets used in the chapter (if publicly available).README.md: A brief description of the chapter's content and instructions on how to run the code.
- Clone the repository to your local machine.
- Navigate to each chapter's folder to review the code and data.
- Follow the instructions in the README.md file within each folder to properly execute the code.
The code in this repository is primarily written in Python. Please ensure that you have Python 3.7 or newer installed along with the following Python libraries:
- numpy
- pandas
- scipy
- matplotlib
- seaborn
- scikit-learn
- pytorch
- torch-geometric
You can install these packages using pip:
pip install numpy pandas scipy matplotlib seaborn scikit-learn pytorch torch-geometricFor any queries or further clarification, please reach out to Haohui Lu at haohui.lu@sydney.edu.au.
This project is licensed under the MIT License - see the LICENSE.md file for details.