---
title: StrokeLine - Stroke Prediction Using Machine Learning
emoji: 🤖
colorFrom: indigo
colorTo: blue
sdk: streamlit
sdk_version: 1.30.0
app_file: app.py
pinned: false
license: mit
---


# Stroke Prediction Using Machine Learning



## About the Project

This project provides a comprehensive machine learning pipeline for predicting the risk of stroke in individuals based on clinical and demographic features. The goal is to enable early identification of high-risk patients, supporting healthcare professionals in making informed decisions and potentially reducing stroke-related morbidity and mortality. The project covers the full data science workflow: data exploration, preprocessing, feature engineering, model selection, hyperparameter optimization, evaluation, explainability, and deployment. The final solution includes a trained model and a Streamlit web application for real-time inference.

---

## About the Dataset

The dataset used is the [Stroke Prediction Dataset](https://www.kaggle.com/datasets/fedesoriano/stroke-prediction-datasett) from Kaggle. It contains 5110 records with 12 features and a binary target variable (`stroke`). The features include:

- **id**: Unique identifier (not used for modeling)
- **gender**: Patient gender (`Male`, `Female`, `Other`)
- **age**: Age in years
- **hypertension**: Hypertension status (`0`: No, `1`: Yes)
- **heart_disease**: Heart disease status (`0`: No, `1`: Yes)
- **ever_married**: Marital status (`Yes`, `No`)
- **work_type**: Type of work (`children`, `Govt_job`, `Never_worked`, `Private`, `Self-employed`)
- **Residence_type**: Living area (`Urban`, `Rural`)
- **avg_glucose_level**: Average glucose level
- **bmi**: Body mass index (may contain missing values)
- **smoking_status**: Smoking behavior (`formerly smoked`, `never smoked`, `smokes`, `Unknown`)
- **stroke**: Target variable (`1`: Stroke occurred, `0`: No stroke)

The dataset is imbalanced, with far fewer positive stroke cases than negatives, and contains missing values in the `bmi` column.

---

## Notebook Summary

The notebook documents the entire process:

1. **Problem Definition**: Outlines the clinical motivation, dataset, and challenges.
2. **EDA**: Visualizes distributions, checks for missing values, and explores feature-target relationships.
3. **Feature Engineering**: Handles missing data, encodes categorical variables, and examines feature correlations.
4. **Data Balancing**: Uses RandomUnderSampler and SMOTE to address class imbalance.
5. **Model Selection**: Compares Random Forest, SVM, and XGBoost classifiers.
6. **Hyperparameter Tuning**: Uses Optuna for automated optimization of XGBoost.
7. **Evaluation**: Reports F1 score, confusion matrix, and classification report.
8. **Explainability**: Applies SHAP for model interpretation.
9. **Model Export**: Saves the trained model for deployment.

---

## Model Results

### Preprocessing

- **Missing Values**: Imputed missing `bmi` values with the mean.
- **Categorical Encoding**: Used `OrdinalEncoder` to convert categorical features to numeric.
- **Feature Selection**: Dropped the `id` column and checked for highly correlated features.

### Data Balancing

- **RandomUnderSampler**: Reduced the majority class to 10% of its original size.
- **SMOTE**: Oversampled the minority class to achieve a 1:1 ratio.

### Training

- **Train-Test Split**: Stratified split to preserve class distribution.
- **Model Comparison**: Evaluated Random Forest, SVM, and XGBoost on balanced data.
- **Best Model**: XGBoost achieved the highest F1 score.

### Hyperparameter Tuning

- **Optuna**: Ran 50 trials to optimize XGBoost hyperparameters (e.g., `n_estimators`, `max_depth`, `learning_rate`, `gamma`, etc.) using 5-fold cross-validation and F1 score as the metric.

### Evaluation

- **F1 Score**: Achieved ~90% F1 score on the balanced test set.
- **Confusion Matrix**: Demonstrated balanced sensitivity and specificity.
- **Classification Report**: Provided detailed precision, recall, and F1 for each class.
- **Explainability**: SHAP analysis identified the most influential features and provided local/global interpretability.

---

## How to Install

Follow these steps to set up the project using a virtual environment:

```bash
# Clone or download the repository
git clone https://github.com/DeepActionPotential/StrokeLineAI
cd StrokeLineAI

# Create a virtual environment
python -m venv venv

# Activate the virtual environment
# On Windows:
venv\Scripts\activate
# On macOS/Linux:
source venv/bin/activate

# Upgrade pip
pip install --upgrade pip

# Install dependencies
pip install -r requirements.txt
```

---

## How to Use the Software

1. **Run the Web Application**  
   Start the Streamlit app:

   ```bash
   streamlit run app.py
   ```

2. **Demo**  
   ## [demo-video](demo/strokeline_demo.mp4)
   ![demo-screenshot](demo/strokeline_demo.jpeg))

---

## Technologies Used

### Data Science & Model Training


- **matplotlib, seaborn**: Data visualization.
- **scikit-learn**: Preprocessing, model selection, metrics, and pipelines.
- **imbalanced-learn**: Advanced resampling (SMOTE, RandomUnderSampler) for class balancing.
- **XGBoost**: High-performance gradient boosting for classification.
- **Optuna**: Automated hyperparameter optimization.
- **SHAP**: Model explainability and feature importance analysis.

### Deployment

- **Streamlit**: Rapid web app development for interactive model inference.
- **joblib**: Model serialization for deployment.

---

## License

This project is licensed under the MIT License.  
See the [LICENSE](LICENSE) file for details.