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---
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title: Stroke Prediction Model
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emoji: 🧠
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colorFrom: red
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colorTo: blue
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sdk: docker
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app_file: app.py
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pinned: false
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---
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# Stroke Prediction Model
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This model predicts the risk of stroke based on demographic and health-related features.
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## Model Details
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- **Model Type**: Random Forest Classifier
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- **Training Data**: Healthcare data including age, gender, various diseases, and lifestyle factors
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- **Features**: Age, gender, hypertension, heart disease, marital status, work type, residence type, glucose level, BMI, smoking status
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- **Output**: Probability of stroke risk (0-1) and risk category
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## Usage
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You can use this model through the Hugging Face Inference API:
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```python
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import requests
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API_URL = "https://abdullah1211-ml-stroke.hf.space"
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headers = {"Content-Type": "application/json"}
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def query(payload):
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response = requests.post(API_URL, headers=headers, json=payload)
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return response.json()
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data = {
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"gender": "Male",
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"age": 67,
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"hypertension": 1,
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"heart_disease": 0,
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"ever_married": "Yes",
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"work_type": "Private",
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"Residence_type": "Urban",
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"avg_glucose_level": 228.69,
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"bmi": 36.6,
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"smoking_status": "formerly smoked"
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}
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output = query(data)
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print(output)
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```
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## Response Format
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```json
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{
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"probability": 0.72,
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"prediction": "High Risk",
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"stroke_prediction": 1
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}
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```
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## Risk Categories
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- Very Low Risk: probability < 0.2
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- Low Risk: probability between 0.2 and 0.4
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- Moderate Risk: probability between 0.4 and 0.6
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- High Risk: probability between 0.6 and 0.8
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- Very High Risk: probability > 0.8 |
