CW-B: Class Weighted Boosting Framework for Imbalance Resilient Multi Class Cardiac Phenotyping
2026-06-29 • Machine Learning
Machine LearningArtificial Intelligence
AI summaryⓘ
The authors developed a new method called CW-B to help identify different types of heart patients when they leave the hospital, even if some patient data is missing or unbalanced. Their approach uses a special way of giving more attention to important but rare heart conditions and handles missing information carefully. They tested it against other computer programs and found that CW-B was better at correctly recognizing the important heart disease groups. This method aims to make heart patient care more accurate and practical in real-world hospital settings.
cardiac discharge phenotypingclass imbalancemissing dataXGBoostclass weightingcross-validationmacro-F1 scorebalanced accuracyclinical decision supportmachine learning interpretability
Authors
Sijia Li, Xiaoyu Tan, Chen Zhan, Yuanji Ma, Haoyu Wang, Xihe Qiu
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.