| --- |
| license: apache-2.0 |
| tags: |
| - healthcare |
| - hospital-readmission |
| - fairness-evaluation |
| - bias-detection |
| - ai-ethics |
| --- |
| |
| # Hospital Readmission Risk - Phase 5: Fairness Evaluation Results |
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| This repository contains the results from Phase 5: Fairness Evaluation & Deployment Readiness. |
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| ## Contents |
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| ### 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 |
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| ## Fairness Metrics Evaluated |
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| ### Demographic Parity |
| Measures if intervention rate is similar across demographic groups (±5% tolerance). |
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| ### Equalized Odds |
| Measures if True Positive Rate (TPR) and False Positive Rate (FPR) are similar across groups (±5% tolerance). |
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| ### Equal Opportunity |
| Measures if True Positive Rate (TPR) is similar across groups (±5% tolerance). |
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| ## Statistical Tests |
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| - **Chi-square test**: Tests independence of intervention rate and demographic group |
| - **Two-proportion z-test**: Tests TPR/FPR differences between groups |
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| ## Model Information |
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| - **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) |
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| ## Usage |
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| 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 |
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| ## Deployment Readiness |
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| Review the fairness report to determine if the model is ready for deployment or if bias mitigation strategies are needed. |
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| ## Citation |
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| If you use these results, please cite the hospital readmission risk prediction project. |
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