README.md

metadata

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.

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About the Dataset

The dataset used is the Stroke Prediction Dataset 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.

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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.

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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.

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How to Install

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

# 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

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How to Use the Software

1. Run the Web Application
   Start the Streamlit app:

   streamlit run app.py
   

2. Demo

   demo-video

   )

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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.

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License

This project is licensed under the MIT License.
See the LICENSE file for details.