Andrews-Osei/breast-cancer-prediction-system_New
Domain:
healthcareRecord type:
model
Creator:
And
Host:
AI-powered breast cancer prediction app - Group 3 Thrive Africa Project
# 🎗️ Breast Cancer Prediction System
### 🚀 **LAUNCH LIVE APP** 🚀
*Click above to try the application - no installation required!*
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An AI-powered web application for breast cancer prediction using machine learning. This project was developed by **Group 3** as part of the **Thrive Africa** learning program.
## 🌐 Live Application
**🚀 Try the app now:** andrews-breast-cancer-predi…
> Access our AI-powered breast cancer prediction tool directly in your browser - no installation needed!
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!\Python
!\Streamlit
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\## 👥 Team Members (Group 3)
\*\*👨💼 Team Leader:\*\* Osei Andrews
\*\*Team Members:\*\*
\- Osei Andrews
\- Obeng Godfred
\- Solace Kumi
\- Addai Kingsford Boateng
\- Azeko Emmanuel
\- Portia Bentum
\- Abigail Aboagyewaa Osei
\- Prince Louis Appiah
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\## 🎯 Project Overview
This application uses machine learning to predict whether a breast mass is \*\*benign\*\* or \*\*malignant\*\* based on 30 features extracted from digitized images of fine needle aspirate (FNA) of breast masses.
\### 📊 Model Performance
| Metric | Score |
|--------|-------|
| \*\*Accuracy\*\* | 97.4% |
| \*\*Precision\*\* | 100% |
| \*\*Recall\*\* | 92.9% |
| \*\*F1 Score\*\* | 96.3% |
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\## 📚 Dataset Information
The application uses the \*\*Breast Cancer Wisconsin (Diagnostic) Dataset\*\* from the UCI Machine Learning Repository.
\*\*Features (30 total):\*\*
\- 🔵 \*\*Mean measurements\*\* (10 features)
\- 🟢 \*\*Standard error measurements\*\* (10 features)
\- 🔴 \*\*Worst/largest measurements\*\* (10 features)
Each category includes:
\- Radius
\- Texture
\- Perimeter
\- Area
\- Smoothness
\- Compactness
\- Concavity
\- Concave points
\- Symmetry
\- Fractal dimension
\*\*📖 Citation:\*\* Wolberg, W., Street, W., \& Mangasarian, O. (1995). Breast Cancer Wisconsin (Diagnostic) Dataset. UCI Machine Learning Repository.
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\## 🚀 Installation \& Setup
\### Prerequisi …
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