---
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.

### This project demonstrates the potential of machine learning to improve healthcare outcomes by providing a user-friendly tool for early stroke risk detection.