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🏥 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

🎯 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

🧠 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

📊 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

📁 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

⚙️ 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

    📧 kyle.s.delivery@gmail.com

    🌐 LinkedIn

    💻 GitHub