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auphong2707/
hospital-readmission-phase2-rf

Name: hospital-readmission-phase2-rf
Availability: InStock

readmission-prediction

lgbm

tabular-classification

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hospital-readmission-phase2-rf - Hospital Readmission Risk Prediction

Model Description

This hospital-readmission-phase2-rf 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: 2025-12-15 19:30:35
Environment: kaggle (CPU)

Performance Metrics

Cross-Validation Results (5-Fold CV)

Metric	Value
Mean ROC-AUC	0.8295 ± 0.0058

Final Test Set Results

Primary Metrics

Metric	Value
ROC-AUC	0.8326
PR-AUC	0.3641
F1 Score	0.4324

Classification Metrics

Metric	Value
Precision	0.3540
Recall	0.5552

Clinical Metrics

Metric	Value
Sensitivity (TPR)	0.5552
Specificity (TNR)	0.8727

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

python

{
  "n_estimators": 500,
  "max_depth": null,
  "min_samples_split": 10,
  "min_samples_leaf": 4,
  "max_features": "sqrt",
  "class_weight": {
    "0": 1,
    "1": 8
  },
  "bootstrap": true,
  "oob_score": true
}

Training Details

Total Training Time: 343.65 minutes
Hyperparameter Search Time: 319.03 minutes
Cross-Validation Folds: 5
Early Stopping: Yes
Device: CPU

Usage

Loading the Model

python

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

bibtex

@misc{hospital-readmission-phase2-lgbm,
  author = {Your Name},
  title = {LightGBM Model for Hospital Readmission Prediction},
  year = {2025},
  url = {https://huggingface.co/your-repo}
}

Dataset Citation