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Breast Cancer Risk Prediction

A machine learning web application that predicts breast cancer risk from tumor cell measurements using logistic regression implemented from scratch. Built with Streamlit and achieving 98.6% accuracy on the Wisconsin Diagnostic Breast Cancer dataset.

Part of the AI Conquer program by AI Vietnam.

Streamlit App

Quick Start

# Install dependencies
pip install -r requirements.txt

# Run the app
streamlit run app.py

Open http://localhost:8501 in your browser. Use the "Benign Example" / "Malignant Example" buttons to quickly test with realistic data.

Features

  • 30-feature prediction — Input FNA biopsy measurements, get instant benign/malignant classification
  • Interactive charts — Radar chart, feature contribution bar chart, confusion matrix, ROC curve
  • Bilingual — Full English and Vietnamese support
  • Light / Dark theme — Toggle between themes
  • PDF report — Download a printable prediction report
  • Model transparency — View accuracy, precision, recall, F1, AUC with live-computed metrics
  • Feature glossary — Detailed explanation of each measurement
  • Sample data loader — One-click auto-fill with realistic examples

Model Performance

Metric Value
Accuracy 98.6%
Precision 97.2%
Recall 97.6%
F1-Score 97.4%
AUC 0.998

Trained from scratch using gradient descent — no sklearn.LogisticRegression. Weights and standardisation parameters are embedded directly in the code (31 parameters total).

Project Structure

├── app.py                  # Streamlit entry point
├── requirements.txt        # Dependencies
├── SHOWCASE.md             # Detailed project showcase
├── src/                    # Application modules
│   ├── model.py            # Weights, prediction, metrics
│   ├── config.py           # Feature definitions, glossary
│   ├── translations.py     # EN/VI translations
│   ├── charts.py           # Plotly chart builders
│   ├── pdf_report.py       # PDF generation
│   └── theme.py            # CSS theme system
├── legacy/                 # Original HTML/JS/CSS version
│   ├── index.html
│   ├── script.js
│   └── style.css
└── docs/                   # Slides and images
    ├── ProjectSlide.pptx
    └── images/

How It Works

  1. Input — Enter 30 tumor measurements from FNA biopsy (mean, std error, worst for 10 base features)
  2. Standardise — Z-score normalisation with pre-computed mean and standard deviation
  3. Predict — Linear combination with trained weights, sigmoid activation
  4. Classify — P(malignant) >= 50% = malignant, otherwise benign

Technology Stack
Python Core language
Streamlit Web framework
NumPy Numerical computation
Plotly Interactive charts
scikit-learn Model evaluation metrics
fpdf2 PDF report generation

Links

Team

Educational project — not a substitute for professional medical diagnosis.