Breast_Cancer_Survival-Analysis

DSTI, MsC Applied Data Science Project

This project aims to analyse and predict the survival of breast cancer patients using various statistical methods and machine learning models. The analysis includes data processing, visualization, and model building to explore factors that influence patient survival.

Table of Contents

• Installation
• Usage
• Features
• Project Structure
• Contributing
• License
• Acknowledgements
• Contact

Installation

1. Clone the repository:

   git clone https://github.com/Brandt-DSTI/Breast_Cancer_Survival-Analysis.git
   cd Breast_Cancer_Survival-Analysis
   

2. Ensure you have R installed (version 4.3.1 recommended).

3. Install dependencies:

install.packages("renv") renv::restore()

Open the project in RStudio:

Open Breast_Cancer_Survival_Analysis.Rproj.

Usage

To run the main analysis: Run the script to generate the outputs.

You can also view the full analysis report here: https://github.com/Brandt-DSTI/Breast_Cancer_Survival-Analysis/blob/main/Report/Survival%20Analysis%20Report_Team%207.pdf

Features

Survival Analysis: Perform Cox proportional hazards modeling, and more. Data Visualisation: Generate plots and visual summaries of the data. Modeling: Implement machine learning models to predict patient survival. Interactive Reports: Render reports in HTML and Markdown formats for easy sharing and visualisation.

Project Structure

/Breast_Cancer_Survival-Analysis/
├── R/                       # R code and scripts for analysis
├── Report/                  # Summarised analysis of project
├── data/                    # Raw data
├── docs/                    # HTML and markdown of scripts
├── .Rprofile                # To load packages
├── README.md                # Project overview and instructions
└── renv.lock                # Lockfile for package dependencies

Contributing

Contributions are welcome! Please open an issue or submit a pull request.

1. Fork the project.
2. Create your feature branch (git checkout -b feature/AmazingFeature).
3. Commit your changes (git commit -m 'Add some AmazingFeature').
4. Push to the branch (git push origin feature/AmazingFeature).
5. Open a pull request.

See CONTRIBUTING.md for more details.

License

Distributed under the MIT License. See LICENSE for more information.

Acknowledgements

Thanks to DSTI for providing the educational environment that inspired this project. https://www.datasciencetech.institute/

Uses the survival, survminer, ggplot2, and other R packages.

Contact

Brandt-DSTI - [email protected]

Project Link: https://github.com/Brandt-DSTI/Breast_Cancer_Survival-Analysis