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language: en
tags:
- healthcare
- stroke-prediction
- medical
license: mit
datasets:
- stroke-prediction
model-index:
- name: Stroke Risk Prediction Model
results:
- task:
type: binary-classification
name: stroke prediction
metrics:
- type: accuracy
value: 0.95
- type: f1
value: 0.82
---
# Stroke Risk Prediction Model
This model predicts the likelihood of a person experiencing a stroke based on various health and demographic features.
## Model Description
The model is a Random Forest classifier trained on healthcare data to predict stroke risk and categorize individuals into risk levels.
### Input
The model accepts the following features:
- **gender**: Male, Female, Other
- **age**: Age in years (numeric)
- **hypertension**: Whether the patient has hypertension (0: No, 1: Yes)
- **heart_disease**: Whether the patient has heart disease (0: No, 1: Yes)
- **ever_married**: Whether the patient has ever been married (Yes/No)
- **work_type**: Type of work (Private, Self-employed, Govt_job, children, Never_worked)
- **Residence_type**: Type of residence (Urban/Rural)
- **avg_glucose_level**: Average glucose level in blood (mg/dL)
- **bmi**: Body Mass Index
- **smoking_status**: Smoking status (formerly smoked, never smoked, smokes, Unknown)
### Output
The model outputs:
- **probability**: Numerical probability of stroke (0-1)
- **prediction**: Risk category (Very Low Risk, Low Risk, Moderate Risk, High Risk, Very High Risk)
- **stroke_prediction**: Binary prediction (0: No stroke, 1: Stroke)
### Limitations and Biases
- The model was trained on a dataset that may have demographic limitations
- Performance may vary across different population groups
- This model should be used as a screening tool only and not as a definitive medical diagnosis
## Usage
```python
import requests
API_URL = "https://api-inference.huggingface.co/models/Abdullah1211/ml-stroke"
headers = {"Authorization": "Bearer YOUR_API_TOKEN"}
def query(payload):
response = requests.post(API_URL, headers=headers, json=payload)
return response.json()
data = {
"gender": "Male",
"age": 67,
"hypertension": 1,
"heart_disease": 0,
"ever_married": "Yes",
"work_type": "Private",
"Residence_type": "Urban",
"avg_glucose_level": 228.69,
"bmi": 36.6,
"smoking_status": "formerly smoked"
}
output = query(data)
``` |