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 reportoutputs/group_metrics_*.csv: Performance metrics by demographic group (race, gender, age)outputs/statistical_tests.json: Statistical significance tests for bias detectionoutputs/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.
