README.md

---
license: apache-2.0
tags:
- healthcare
- hospital-readmission
- fairness-evaluation
- bias-detection
- ai-ethics
---

# Hospital Readmission Risk - Phase 5: Fairness Evaluation Results

This repository contains the results from Phase 5: Fairness Evaluation & Deployment Readiness.

## Contents

### Outputs
- `outputs/fairness_report.json`: Comprehensive fairness evaluation report
- `outputs/group_metrics_*.csv`: Performance metrics by demographic group (race, gender, age)
- `outputs/statistical_tests.json`: Statistical significance tests for bias detection
- `outputs/risk_categories_*.csv`: Risk category distribution by demographic group

## Fairness Metrics Evaluated

### Demographic Parity
Measures if intervention rate is similar across demographic groups (±5% tolerance).

### Equalized Odds
Measures if True Positive Rate (TPR) and False Positive Rate (FPR) are similar across groups (±5% tolerance).

### Equal Opportunity
Measures if True Positive Rate (TPR) is similar across groups (±5% tolerance).

## Statistical Tests

- **Chi-square test**: Tests independence of intervention rate and demographic group
- **Two-proportion z-test**: Tests TPR/FPR differences between groups

## Model Information

- **Model**: Gradient Boosting (LightGBM) with Platt Calibration
- **Optimal Threshold**: From Phase 4 ROI analysis
- **Test Set**: 15,265 patients
- **Demographics**: Race (6 categories), Gender (3 categories), Age (10 ranges)

## Usage

These results can be used for:
- Assessing model fairness before deployment
- Identifying potential bias in predictions
- Determining if bias mitigation is needed
- Creating model cards with fairness documentation
- Meeting regulatory requirements for AI fairness

## Deployment Readiness

Review the fairness report to determine if the model is ready for deployment or if bias mitigation strategies are needed.

## Citation

If you use these results, please cite the hospital readmission risk prediction project.