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

2026-06-29

AI Summary

This study addresses the critical challenge of missed diagnosis of high-risk cardiac discharge phenotypes in real-world clinical settings, where data scarcity and class imbalance are prevalent. To this end, the authors propose a clinically risk-aligned, class-weighted XGBoost framework that integrates instance-level class weighting guided by clinical priorities, explicit modeling of missing values via missingness indicators, and a class-level error auditing mechanism. This approach enhances the model’s ability to identify minority high-risk phenotypes while preserving interpretability. Evaluated under five-fold stratified cross-validation, the proposed method significantly outperforms state-of-the-art tree-based models, ensemble techniques, and neural network baselines across multiple metrics, including Accuracy, Macro-F1, Balanced Accuracy, and Prioritized F1.

Abstract

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 improve recognition of clinically prioritized phenotypes while preserving interpretable and auditable decision logic. In five-fold stratified cross-validation, CW-B achieves the best Accuracy, Macro-F1, Balanced Accuracy, and Prioritized F1 among tree-based, ensemble, and neural baselines. Overall, CW-B provides a practical and deployment-oriented approach for more reliable cardiac discharge phenotyping in real-world clinical settings.

Problem

• class imbalance
• cardiac phenotyping
• missing data
• high-risk phenotype
• real-world clinical data

Innovation

• class-weighted boosting
• imbalanced learning
• missingness augmentation
• cardiac phenotyping
• interpretable machine learning