Breast Cancer Predictor
About this Project
🔬 This project leverages Machine Learning to assist in early breast cancer detection. Built with Streamlit, it allows users to input values or upload CSV files 📂 for instant predictions. The app provides real-time results 📊 along with downloadable reports, ensuring both accuracy and usability. ⚡ Designed with a minimal and clean interface for easy understanding, this project demonstrates how AI can make healthcare more accessible 💡.
Key Features
- •Real-time breast cancer risk prediction using machine learning algorithms
- •Interactive web interface built with Streamlit for easy data input
- •Support for both manual data entry and CSV file uploads
- •Downloadable prediction reports with detailed analysis
- •Clean, minimal UI designed for healthcare professionals and patients
- •High accuracy model trained on validated medical datasets
Challenges & Solutions
The main challenges were ensuring high model accuracy while maintaining interpretability for medical professionals, handling missing data in patient records, and creating a user-friendly interface that could be used by both technical and non-technical users. I also had to ensure the model met medical compliance standards.
What I Learned
This project taught me the importance of model interpretability in healthcare AI, the critical need for data privacy and security in medical applications, and how to balance technical complexity with user accessibility. I also gained experience in deploying ML models for real-world healthcare scenarios.