🏥 Predicting Hospital Readmissions with Machine Learning

This project predicts patient hospital readmission using the UCI Diabetes 130-US Hospitals dataset. It applies advanced machine learning techniques to identify whether a patient is likely to be readmitted:

• 0 → Not readmitted
• 1 → Readmitted within 30 days
• 2 → Readmitted after 30 days

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🎯 Project Goals

• Clean and preprocess a real-world healthcare dataset
• Perform exploratory data analysis (EDA)
• Engineer relevant features
• Handle class imbalance
• Build a multiclass classification model using XGBoost
• Tune hyperparameters using Optuna
• Evaluate and interpret model performance

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🧠 Machine Learning Approach

✅ Techniques Used

• XGBoost multiclass classification
• Sample weighting to address class imbalance
• Hyperparameter tuning with Optuna
• Performance evaluation with classification reports and confusion matrices

🧪 Label Encoding Logic

Label	Meaning
0	Not readmitted
1	Readmitted within 30 days
2	Readmitted after 30 days

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📊 Results

Metric	Value
Accuracy	52.0%
Macro F1 Score	0.45
Class 1 Recall	31.0%
Model	Tuned XGBoost (via Optuna)

• Optuna tuning improved macro F1 and recall for class 1 (early readmission)
• Class imbalance was addressed using sample weighting

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📁 Project Structure

Healthcare_Analytics_Simulation/ ├── data/ # Raw and sample data (not tracked in Git) ├── notebooks/ │ ├── 01_EDA.ipynb # Exploratory data analysis │ ├── 02_Modeling_XGBoost.ipynb # Initial modeling attempts │ └── 03_Hyperparameter_Tuning_Optuna.ipynb ├── models/ │ └── best_xgb_model.json # Trained model ├── src/ │ ├── preprocessing.py # Feature engineering and encoding │ ├── train_model.py # Model training script ├── requirements.txt ├── README.md └── .gitignore

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⚙️ How to Run

1. Clone the repo

git clone https://github.com/your-username/Healthcare_Analytics_Simulation.git
cd Healthcare_Analytics_Simulation

2. Install dependencies

pip install -r requirements.txt

3. Run notebooks

Open notebooks in Jupyter or VSCode to explore and reproduce results.
📦 Requirements

    Python 3.12+

    XGBoost

    Scikit-learn

    Optuna

    Pandas, NumPy, Matplotlib

Install all requirements:

pip install -r requirements.txt

📌 Key Learnings

    How to handle class imbalance in multiclass problems

    How to tune hyperparameters using Optuna

    How to balance precision/recall tradeoffs in clinical data

    How to structure and document ML projects for recruiters

📜 License

This project is for educational and portfolio purposes only. Not intended for clinical use.
🙋‍♂️ Author

Kyle Spengler

    📧 [email protected]

    🌐 LinkedIn

    💻 GitHub