hospital-readmission-logistic-regression - Hospital Readmission Risk Prediction

Model Description

This hospital-readmission-logistic-regression 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: 2025-12-10 05:49:14
Environment: kaggle (CPU)

Performance Metrics

Cross-Validation Results (5-Fold CV)

Metric	Value
Mean ROC-AUC	0.8115 ± 0.0062

Final Test Set Results

Primary Metrics

Metric	Value
ROC-AUC	0.8075
PR-AUC	0.3009
F1 Score	0.4066

Classification Metrics

Metric	Value
Precision	0.2782
Recall	0.7553

Clinical Metrics

Metric	Value
Sensitivity (TPR)	0.7553
Specificity (TNR)	0.7538

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

{
  "C": 0.1,
  "class_weight": "balanced",
  "max_iter": 2000,
  "penalty": "l1",
  "solver": "liblinear"
}

Training Details

• Total Training Time: 110.84 minutes
• Hyperparameter Search Time: 0.00 minutes
• Cross-Validation Folds: 5
• Early Stopping: Yes
• Device: CPU

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.

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