Breast Cancer Classification using Neural Networks

This project implements a neural network-based classifier to distinguish between benign and malignant breast cancer cases using a public dataset.

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Table of Contents

1. Overview
2. Problem Statement
3. Dataset
4. Preprocessing
5. Model Architecture
6. Evaluation Metrics
7. SDG Alignment
8. Requirements
9. Installation
10. Usage
11. Results
12. Contributing
13. Acknowledgements

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Overview

Early and accurate diagnosis of breast cancer significantly increases survival rates. This project uses a neural network model to classify breast cancer cases based on diagnostic features. The model is trained and evaluated using standardised machine learning workflows.

Problem Statement

Breast cancer remains one of the leading causes of cancer-related deaths among women worldwide. The challenge is to classify cancer diagnoses accurately using predictive modelling to support early intervention.

Dataset

The dataset used is derived from the Breast Cancer Wisconsin Diagnostic Dataset, containing features computed from digitised images of a fine needle aspirate (FNA) of a breast mass.

• Target Labels:
  • 0 → Malignant
  • 1 → Benign

Preprocessing

• Feature scaling using standardisation (zero mean, unit variance)
• Splitting the dataset into training and testing sets
• Encoding labels for binary classification

Model Architecture

The neural network is built using a feedforward design and includes:

• Input layer corresponding to the number of features
• One or more hidden layers with ReLU activation
• Output layer with sigmoid activation for binary classification

Evaluation Metrics

• Accuracy
• Precision
• Recall
• F1-score
• Confusion Matrix

SDG Alignment

Goal	Relevance
SDG 3: Good Health and Well-being	Enhances early detection and diagnosis through AI, aiding in reducing mortality and promoting well-being.
SDG 9: Industry, Innovation and Infrastructure	Utilises innovative neural network algorithms and contributes to healthcare infrastructure through data-driven insights.

Requirements

• Python 3.8+
• scikit-learn
• pandas
• numpy
• tensorflow / keras
• matplotlib
• seaborn

Installation

Clone the repository and install dependencies:

git clone https://github.com/your-username/breast-cancer-classification-nn.git
cd breast-cancer-classification-nn
pip install -r requirements.txt

Usage

To run the model and replicate results:

jupyter notebook BreastCancerClassificationNN.ipynb

Follow the notebook steps for data loading, preprocessing, model training, and evaluation.

Results

The model demonstrates high classification performance on the test set, achieving:

• Accuracy: >95%
• Low false positives and false negatives

Contributing

Feel free to fork the repository and submit pull requests. Please ensure contributions are well-documented and tested.

Acknowledgements

• Dataset provided by UCI Machine Learning Repository
• Thanks to all open-source contributors