---
license: gpl
task_categories:
- text-classification
language:
- en
tags:
- biology
- stroke
- hypertension
- africa
size_categories:
- 1K<n<10K
---
This dataset contains clinical and demographic information of patients along with their stroke status. The dataset provides comprehensive medical data for stroke research and machine learning applications.

## Dataset Summary

- **Total Samples**: 5,110
- **Features**: 12 (Patient ID, Demographic info, Clinical measurements, Stroke status)
- **Task**: Binary classification (Stroke vs No Stroke)

## Features

### Patient Information
- **id**: Unique identifier (integer)
- **gender**: Biological sex (Male/Female)
- **age**: Age in years (integer)
- **ever_married**: Marital status (Yes/No)
- **work_type**: Type of work (Private, Self-employed, Govt_job, children, Never_worked)
- **Residence_type**: Type of residence (Urban/Rural)

### Medical Information
- **hypertension**: Hypertension status (0=No, 1=Yes)
- **heart_disease**: Heart disease status (0=No, 1=Yes)
- **avg_glucose_level**: Average glucose level in blood (mg/dL)
- **bmi**: Body Mass Index (kg/m²)
- **smoking_status**: Smoking status (never smoked, formerly smoked, smokes, Unknown)
- **stroke**: Target variable - stroke occurrence (0=No, 1=Yes)

## Dataset Statistics

### Stroke Distribution
- **No Stroke (0)**: 91.0%
- **Stroke (1)**: 9.0%

### Demographics
- **Gender Distribution**:
  - Female: 58.6%
  - Male: 41.4%
- **Age Statistics**:
  - Range: 0-82 years
  - Mean: 36.1 years
  - Median: 33 years
- **Residence Type**:
  - Rural: 60.0%
  - Urban: 40.0%

### Medical Statistics
- **Hypertension**: 30.0%
- **Heart Disease**: 15.0%
- **Average BMI**: 30.9 kg/m²
- **Average Glucose Level**: 121.1 mg/dL

### Work Type Distribution
- Private: 40.0%
- Self-employed: 40.0%
- Govt_job: 10.0%
- children: 5.0%
- Never_worked: 5.0%

### Smoking Status
- Never smoked: 80.0%
- Formerly smoked: 10.0%
- Currently smokes: 8.0%
- Unknown: 2.0%

## Usage

### Data Exploration

```python
# View stroke distribution
print("Stroke distribution:")
print(df['stroke'].value_counts(normalize=True) * 100)

# View gender distribution
print("\nGender distribution:")
print(df['gender'].value_counts(normalize=True) * 100)

# Summary statistics for numerical features
print("\nNumerical features summary:")
print(df[['age', 'avg_glucose_level', 'bmi']].describe())
```

### Machine Learning Example

```python
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report
import pandas as pd

# Prepare features and target
feature_cols = ['gender', 'age', 'hypertension', 'heart_disease', 'ever_married',
               'work_type', 'Residence_type', 'avg_glucose_level', 'bmi', 'smoking_status']

X = df[feature_cols]
y = df['stroke']

# Encode categorical variables
X = pd.get_dummies(X, columns=['gender', 'ever_married', 'work_type', 'Residence_type', 'smoking_status'])

# Split the data
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42, stratify=y
)

# Train a model
model = RandomForestClassifier(random_state=42, class_weight='balanced')
model.fit(X_train, y_train)

# Evaluate
y_pred = model.predict(X_test)
print(classification_report(y_test, y_pred))