https://github.com/vishalgaud17/stroke
A simple Streamlit web app that predicts stroke risk based on user input features like age, BMI, glucose level, and lifestyle factors, using a pre-trained machine learning model.
https://github.com/vishalgaud17/stroke
machine-learning numpy pandas python scikit-learn streamlit
Last synced: 10 months ago
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A simple Streamlit web app that predicts stroke risk based on user input features like age, BMI, glucose level, and lifestyle factors, using a pre-trained machine learning model.
- Host: GitHub
- URL: https://github.com/vishalgaud17/stroke
- Owner: VishalGaud17
- Created: 2025-04-28T09:41:28.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2025-04-28T09:54:44.000Z (10 months ago)
- Last Synced: 2025-04-28T10:49:59.283Z (10 months ago)
- Topics: machine-learning, numpy, pandas, python, scikit-learn, streamlit
- Language: Jupyter Notebook
- Homepage: https://stroke-awz8xzb8pnmmtekkn2yd4e.streamlit.app/
- Size: 213 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 🧠 Stroke Risk Prediction App
An interactive Streamlit web app that predicts the risk of stroke based on user health and lifestyle information.
It uses a machine learning model trained on healthcare data to provide instant predictions.
---
## 🚀 Features
- Predicts stroke risk based on:
- Age
- BMI
- Average glucose level
- Gender
- Smoking status
- Hypertension
- Heart disease
- Marital status
- Residence type
- Work type
- Friendly and simple user interface.
- Instant feedback with health advisory messages.
- Error handling for model loading.
---
## 🛠️ Tech Stack
- Python
- Streamlit
- Pickle (for loading ML model)
- NumPy
---
## 📥 Setup Instructions
1. **Clone this repository:**
```bash
git clone https://github.com/VishalGaud17/Stroke.git
cd Stroke
```
2. **Install dependencies:**
```bash
pip install -r requirements.txt
```
3. **Run the Streamlit app:**
```bash
streamlit run stroke.py
```
---
## 📂 Project Structure
```plaintext
Stroke/
├── stroke.py # Main Streamlit application
├── stroke.pkl # Pre-trained machine learning model
└── README.md # Project documentation (you are reading it)
```
---
## 🤔 How It Works
1. Enter your details such as age, BMI, glucose level, etc.
2. The app encodes the inputs appropriately.
3. Inputs are passed to the machine learning model.
4. The model predicts whether there is a stroke risk.
5. An advisory message is displayed based on the prediction.
---
## 👨💻 Author
- [Vishal Gaud](https://github.com/VishalGaud17)
---
## ⭐ Support
If you find this project useful, please consider giving it a ⭐ on GitHub!
---
## ⚠️ Disclaimer
This app is for educational and informational purposes only.
It is not a substitute for professional medical advice, diagnosis, or treatment.
Always seek the advice of your physician or other qualified health provider for any medical condition.