hospital-readmission-lgbm - Hospital Readmission Risk Prediction

Model Description

This hospital-readmission-lgbm model predicts the risk of 30-day hospital readmission for diabetic patients. The model was trained on the UCI Diabetes 130-US Hospitals dataset with robust cross-validation and comprehensive evaluation.

Task: Hospital 30-Day Readmission Risk Prediction
Model Type: Gradient Boosting Machine (LightGBM)
Training Date: 2025-12-01 18:33:39
Environment: kaggle (GPU)

Performance Metrics

Cross-Validation Results (5-Fold CV)

Metric	Value
Mean ROC-AUC	0.8399 ± 0.0055

Final Test Set Results

Primary Metrics

Metric	Value
ROC-AUC	0.8424
PR-AUC	0.4000
F1 Score	0.1053

Classification Metrics

Metric	Value
Precision	0.6978
Recall	0.0569

Clinical Metrics

Metric	Value
Sensitivity (TPR)	0.0569
Specificity (TNR)	0.9969

Model Visualizations

ROC Curve

Precision-Recall Curve

Confusion Matrix

Calibration Curve

Feature Importance

Learning Curves

Validation Curves

Cross-Fold Metrics Comparison

Dataset Information

Property	Value
Total Samples	101,766
Features	113
Development Set	86,501
Final Test Set	15,265

Training Configuration

Evaluation Pipeline

Final Holdout Split: Stratified split into development and test sets
Hyperparameter Search: Grid search with 5-fold cross-validation
Nested Early Stopping: Inner validation split within each fold
Final Evaluation: Untouched holdout test set

Best Hyperparameters

{
  "n_estimators": 150,
  "learning_rate": 0.05,
  "num_leaves": 31,
  "max_depth": -1,
  "subsample": 0.9,
  "colsample_bytree": 0.7,
  "reg_alpha": 0.0,
  "reg_lambda": 0.1
}

Training Details

Total Training Time: 215.04 minutes
Hyperparameter Search Time: 127.61 minutes
Cross-Validation Folds: 5
Early Stopping: Yes
Device: GPU

Usage

Loading the Model

import joblib
import pandas as pd

# Load the trained model
model = joblib.load('gradient_boosting_model.joblib')

# Load your preprocessed features
X_new = pd.read_csv('your_features.csv')

# Make predictions
predictions = model.predict(X_new)
probabilities = model.predict_proba(X_new)[:, 1]

Feature Requirements

The model expects preprocessed features from the UCI Diabetes 130-US Hospitals dataset. Features include:

Patient demographics (age, gender, race)
Admission details (admission type, source, length of stay)
Medical history (number of diagnoses, procedures)
Medication information
Lab results (A1c test results, glucose serum test)
Previous utilization (outpatient, inpatient, emergency visits)

See feature_importance.csv for complete feature list and importance scores.

Limitations and Biases

Domain-Specific: Model is trained specifically for diabetic patient readmissions
Dataset Bias: Training data from 130 US hospitals (1999-2008) may not generalize to all healthcare settings
Class Imbalance: Dataset may have imbalanced readmission rates
Temporal Drift: Healthcare practices have evolved since data collection
Geographic Limitation: US-based dataset may not apply to other healthcare systems

Ethical Considerations

This model is intended to assist healthcare providers in identifying patients at risk of readmission. It should:

NOT be used as the sole basis for treatment decisions
Be validated on your specific patient population before deployment
Be monitored for fairness across different demographic groups
Be regularly retrained with recent data to account for changing patterns

Citation

@misc{hospital-readmission-lgbm,
  author = {Your Name},
  title = {LightGBM Model for Hospital Readmission Prediction},
  year = {2025},
  url = {https://huggingface.co/your-repo}
}

Dataset Citation

@misc{strack2014impact,
  title={Impact of HbA1c Measurement on Hospital Readmission Rates: Analysis of 70,000 Clinical Database Patient Records},
  author={Strack, Beata and DeShazo, Jonathan P and Gennings, Chris and Olmo, Juan L and Ventura, Sebastian and Cios, Krzysztof J and Clore, John N},
  journal={BioMed Research International},
  volume={2014},
  year={2014},
  publisher={Hindawi}
}

License

This model is released under the MIT License. The underlying dataset has its own license terms.

Contact

For questions or issues, please open an issue in the repository.

Disclaimer: This model is for research and educational purposes. Always consult healthcare professionals for medical decisions.

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