Breast Cancer SML Project

This project is part of my Statistical Machine Learning coursework.
It analyzes breast cancer diagnosis using features extracted from cell nuclei in fine needle aspiration (FNA) images of breast masses.

The repository contains:

• report.html – A redacted HTML report where the code chunks show placeholders instead of full implementation; figures and outputs are not included.
• script – Source code folder with function signatures and structure only; full code available upon request.
• Dataset description – See below for feature explanations
• Languages Used
  • R
  • HTML

For privacy reasons, the full code and results are not included in this public version. Please contact me to request access to the complete project.

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📊 Dataset Overview

Fine-needle aspiration (FNA) is a medical procedure healthcare providers use to collect cell samples from a suspicious lump or abnormal area of the body.
It’s also called a fine-needle biopsy.
FNA involves extracting cells, tissue, and sometimes fluids using a thin needle and syringe.
The collected sample is then prepared and analyzed — in this case, microscopic images of the extracted cells are processed to calculate diagnostic features.

The dataset contains measurements from fine-needle aspiration (FNA) images of breast mass cell nuclei.
It is widely used in machine learning research to develop models that can distinguish between benign (non-cancerous) and malignant (cancerous) tumors.

The dataset includes 569 samples, each described by 30 numeric features derived from 10 original cell nucleus characteristics.
These features are designed to capture size, shape, texture, and complexity of the cell nuclei — all of which can be indicators of malignancy.

Features

1. Radius – Average distance between the nucleus center and its perimeter points. Larger radius may indicate malignancy.
2. Texture – Standard deviation of gray-scale values. Higher variation can signal irregular texture.
3. Perimeter – Total length around the nucleus.
4. Area – Total area of the nucleus. Malignant nuclei often have larger areas.
5. Smoothness – Local variation in radius lengths. Less smoothness means more irregularity.
6. Compactness – (perimeter² / area) – 1. Measures how compact the nucleus is.
7. Concavity – Severity of concave portions of the contour.
8. Concave Points – Number of concave points along the contour.
9. Symmetry – How symmetrical the nucleus is. Asymmetry can indicate malignancy.
10. Fractal Dimension – Complexity of the nucleus boundary.

For each feature, the dataset includes:

• Mean – Average over all measured nuclei in the sample.
• Standard Error – Variation across nuclei.
• Worst (largest) – Average of the three largest values, emphasizing extremes.

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📂 Project Structure

Breast Cancer SML/
├── figures               # Plots and visual outputs
├── report.html            # Analysis report (exported HTML)
├── script.R               # Main R analysis script
├── LICENSE                # License file
└── README.md              # Project overview

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🚀 How to Run

1. Clone the repository:

git clone https://github.com/tezamo/breast-cancer-sml-project.git
cd breast-cancer-sml-project

2. Run the analysis:

rstudio script.r

📋View the Report

You can see the full rendered report here:
Open Report

📜 License

This project is for educational purposes as part of a coursework submission. Dataset used is from the UCI Machine Learning Repository:

https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)

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📌 Copyright & Code Usage

© 2025 tezamo. All rights reserved.

The source code in this repository is provided for educational purposes as part of my coursework.
You may view and reference the code, but copying, modifying, or redistributing it in any form without explicit permission is prohibited.

If you wish to reuse any part of the code, please contact me to request permission.