Optimized Stroke Prediction with Machine Learning 🚀

📌 Overview

This project aims to predict the likelihood of stroke using machine learning models such as XGBoost, Random Forest, and Logistic Regression. The dataset consists of patient health records with various risk factors, and the model is optimized using Optuna for hyperparameter tuning. Additionally, SHAP (Shapley Additive Explanations) is utilized to enhance model interpretability by identifying the top contributing features.

🛠️ Technologies Used

• Python (Core programming language)
• Scikit-learn (Machine Learning Library)
• XGBoost (Boosted Decision Trees)
• Random Forest (Ensemble Learning)
• Optuna (Hyperparameter Tuning)
• SHAP (Explainable AI)
• SMOTE (Handling Class Imbalance)
• Pandas & NumPy (Data Manipulation)
• Matplotlib & Seaborn (Visualization)

📊 Dataset

• The dataset used in this project contains 5,110 patient records with features like age, hypertension, heart disease, smoking status, glucose level, BMI, and stroke occurrence.
• Data Source: Healthcare Stroke Prediction Dataset

🔥 Key Features

✅ Data Preprocessing & Feature Engineering: Missing value imputation, outlier detection, encoding, and scaling.

✅ Class Imbalance Handling: Augmented minority class representation using SMOTE to balance stroke cases.

✅ Model Optimization & Evaluation: Fine-tuned Random Forest & XGBoost, achieving 97.4% accuracy.

✅ Explainable AI: Used SHAP to highlight the top 5 stroke risk factors.

✅ Hyperparameter Tuning: Optimized model performance using Optuna, improving prediction precision and recall.

⚙️ Installation

To run this project locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/pjaiswalusf/Stroke-Prediction
   cd stroke-prediction-ml

2. Create and activate a virtual environment (optional but recommended):

   python -m venv venv  # Create virtual environment
   source venv/bin/activate  # For Mac/Linux
   venv\Scripts\activate  # For Windows

3. Install dependencies:

   pip install -r requirements.txt

4. Run the Jupyter Notebook or Python script:

   jupyter notebook

   Open stroke_prediction.ipynb and execute the cells.

📌 Usage

• Exploratory Data Analysis (EDA): Run the provided notebook to generate insights and visualize key features.
• Train the Model: Train different models using the dataset and compare performance metrics.
• Hyperparameter Tuning: Fine-tune the models using Optuna for the best results.
• Explainability: Use SHAP to analyze feature importance and understand model predictions.

📈 Model Performance

Model	Accuracy	Precision	Recall	F1-score
XGBoost	97.4%	95.8%	96.1%	96.0%
Random Forest	96.7%	94.2%	95.0%	94.6%
Logistic Regression	85.6%	81.0%	83.2%	82.1%

📊 Feature Importance (Top 5 Features by SHAP Analysis)

1️⃣ Age 🡆 Higher age increases stroke risk. 2️⃣ Hypertension 🡆 People with hypertension have a significantly higher probability of stroke. 3️⃣ Heart Disease 🡆 Patients with pre-existing heart disease are more likely to have a stroke. 4️⃣ Glucose Level 🡆 Elevated blood sugar levels strongly correlate with stroke occurrences. 5️⃣ BMI 🡆 Obesity is a contributing factor to stroke risk.

📌 Future Improvements

🚀 Deploy the model as an API using Flask/FastAPI for real-time stroke prediction. 🚀 Improve class balancing techniques to handle data skewness more effectively. 🚀 Try deep learning models (e.g., LSTMs or Neural Networks) for better accuracy.

🤝 Contributing

Feel free to fork this repository, open issues, or submit pull requests. Contributions are always welcome!

📜 License

This project is open-source and available under the MIT License.

📬 Contact

For any questions or collaborations, reach out to me at jaiswalpratik49@gmail.com or connect via LinkedIn. 🚀