Analysis of FNA Image Data for Breast Cancer Diagnosis

This repository contains a project focused on analyzing data derived from fine needle aspiration (FNA) images to support breast cancer diagnosis. The main objective is to apply supervised and unsupervised learning techniques to classify breast masses as benign or malignant.

Authors

• Claudia Heredia Ceballos
• Manuel Otero Barbasán
• Marta Pineda Gisbert
• Javier Fernández Castillo

Organization

University of Seville

Project Description

The dataset includes 569 samples, categorized as:

• 357 benign cases.
• 212 malignant cases.

Each sample is described by 33 features related to the properties of cell nuclei in the images.

Techniques Used

• Supervised Classification: Algorithms such as CART.
• Unsupervised Clustering: Exploration of latent patterns in the data.

How to Run the Analysis

1. Clone the repository:

   git clone https://github.com/JaviFdez7/FNA-BCA-Diagnosis.git
   cd FNA-BCA-Diagnosis

2. Open the R Markdown file in RStudio:
   • notebook.Rmd
3. Execute the code chunks in the specified order to reproduce the analyses.

Expected Results

• Effective classification of samples as benign or malignant.
• Clustering visualizations highlighting relevant patterns in the data.

License

This project is licensed under the MIT License.