A newer version of the Streamlit SDK is available:
1.44.1
title: Stroke Prediction App Streamlit
emoji: 💻
colorFrom: green
colorTo: gray
sdk: streamlit
sdk_version: 1.36.0
app_file: app.py
pinned: false
license: apache-2.0
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
Early Detection of Stroke Risk with Machine Learning
This project tackles the crucial task of predicting stroke risk using machine learning. It leverages a powerful model called Light Gradient Boosting (LightGBM) to analyze data and identify individuals who might be at higher risk of stroke.
Prioritizing Safety with Recall
Unlike some models, this project prioritizes "recall," meaning it would rather recommend a checkup for a healthy person than miss someone with potential stroke risk. This approach ensures people get the necessary medical attention, even if they ultimately turn out to be healthy.
User-Friendly Experience with Streamlit
The project is built with Streamlit, a framework designed for creating user-friendly web applications. This means the application is accessible and easy to navigate, allowing anyone to assess their potential stroke risk without needing technical expertise.
Overall Benefits
Early Detection: The project empowers proactive healthcare by identifying potential stroke risks early. Prioritized Safety: The focus on recall ensures individuals with potential risk receive necessary checkups. User-Friendly Access: The Streamlit interface makes the tool accessible to a broad audience.