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README.md

@@ -8,62 +8,106 @@ app_file: app.py
 pinned: false
 ---
 
-# Stroke Prediction Model
+# Stroke Risk Prediction Model
 
-This model predicts the risk of stroke based on demographic and health-related features.
+This Hugging Face Space hosts a machine learning model for stroke risk prediction based on various health and demographic factors.
 
-## Model Details
+## Model Overview
 
-- **Model Type**: Random Forest Classifier
-- **Training Data**: Healthcare data including age, gender, various diseases, and lifestyle factors
-- **Features**: Age, gender, hypertension, heart disease, marital status, work type, residence type, glucose level, BMI, smoking status
-- **Output**: Probability of stroke risk (0-1) and risk category
+- **Type**: Random Forest Classifier
+- **Target**: Binary classification (stroke risk)
+- **Output**: Probability of stroke risk + risk category
+- **Features**: Age, gender, hypertension, heart disease, BMI, glucose levels, and more
 
-## Usage
+## API Endpoints
 
-You can use this model through the Hugging Face Inference API:
+### POST `/api/predict`
 
-```python
-import requests
+Predicts stroke risk based on patient health data.
 
-API_URL = "https://abdullah1211-ml-stroke.hf.space"
-headers = {"Content-Type": "application/json"}
+#### Request Format
 
-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"
+```json
+{
+  "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)
-print(output)
 ```
 
-## Response Format
+#### Response Format
 
 ```json
 {
   "probability": 0.72,
   "prediction": "High Risk",
-  "stroke_prediction": 1
+  "stroke_prediction": 1,
+  "risk_factors": [
+    "Advanced Age (>65)",
+    "Very High Blood Glucose (>200)",
+    "Obesity (BMI > 30)",
+    "Former Smoker"
+  ],
+  "important_features": [
+    {"feature": "avg_glucose_level", "importance": 0.31},
+    {"feature": "age", "importance": 0.28},
+    {"feature": "bmi", "importance": 0.15}
+  ],
+  "execution_time_ms": 50,
+  "using_model": true,
+  "model_version": "1.0"
 }
 ```
 
+### GET `/`
+
+Returns information about the model and API usage.
+
+## Parameter Details
+
+| Parameter | Description | Type | Values |
+|-----------|-------------|------|--------|
+| gender | Gender of the patient | String | "Male", "Female", "Other" |
+| age | Age in years | Number | 0-120 |
+| hypertension | Whether patient has hypertension | Number | 0 (No), 1 (Yes) |
+| heart_disease | Whether patient has heart disease | Number | 0 (No), 1 (Yes) |
+| ever_married | Whether patient has ever been married | String | "Yes", "No" |
+| work_type | Type of work/employment | String | "Private", "Self-employed", "Govt_job", "children", "Never_worked" |
+| Residence_type | Type of residence | String | "Urban", "Rural" |
+| avg_glucose_level | Average glucose level in blood (mg/dL) | Number | 50-300+ |
+| bmi | Body Mass Index | Number | 10-50+ |
+| smoking_status | Smoking status of patient | String | "never smoked", "formerly smoked", "smokes", "Unknown" |
+
 ## Risk Categories
 
-- Very Low Risk: probability < 0.2
-- Low Risk: probability between 0.2 and 0.4
-- Moderate Risk: probability between 0.4 and 0.6
-- High Risk: probability between 0.6 and 0.8
-- Very High Risk: probability > 0.8 
+- **Very Low Risk**: < 10% probability 
+- **Low Risk**: 10-20% probability
+- **Moderate Risk**: 20-40% probability
+- **High Risk**: 40-60% probability
+- **Very High Risk**: > 60% probability
+
+## Model Training
+
+This model was trained on a dataset of over 5,000 patients with various health metrics and stroke outcomes. It identifies key risk factors like:
+
+- Age
+- High blood pressure
+- Heart disease
+- Glucose levels
+- BMI
+- Smoking status
+
+## Integration with BrainWise App
+
+This model serves as the backend for the BrainWise app's stroke risk calculator, providing users with risk assessments based on their health metrics.
+
+## Disclaimer
+
+This model is for informational purposes only and should not replace professional medical advice. Always consult with healthcare providers for medical decisions.