CW-B: Class Weighted Boosting Framework for Imbalance Resilient Multi Class Cardiac Phenotyping

Cardiac discharge phenotyping informs post-discharge treatment and follow-up, but real-world records are often incomplete and class-imbalanced, increasing the risk of missed high-risk phenotypes. We propose CW-B, a clinical risk-aligned class-weighted XGBoost pipeline for five-class cardiac discharge phenotyping under real-world class imbalance and missingness. CW-B combines fold-specific class-balanced instance weighting, missingness-indicator augmentation, and classwise error auditing to impro

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ethics.ai (29 June 2026), “CW-B: Class Weighted Boosting Framework for Imbalance Resilient Multi Class Cardiac Phenotyping,” evidence record 453, https://ethics.ai/record/453 (originally published by arXiv).

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