CW-B: Class Weighted Boosting Framework for Imbalance Resilient Multi Class Cardiac Phenotyping
arXiv:2606.29907v1 Announce Type: new 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
The proliferation of deep learning in critical sectors like healthcare, coupled with the inherent messiness of real-world data, is driving the need for robust, imbalance-resilient AI frameworks.
Improving AI's performance in imbalanced or incomplete datasets, especially in high-stakes applications like medical diagnostics, directly enhances reliability and reduces clinical risk, accelerating adoption.
The ability of AI models to provide accurate, risk-aligned phenotyping despite common data imperfections will expand, leading to more trustworthy and impactful clinical decision support systems.
- · Healthcare sector
- · AI/ML developers
- · Patients with complex conditions
- · Medical data scientists
- · Traditional statistical methods
- · AI models lacking robustness to imbalance
- · Manual diagnostic processes
Improved early diagnosis and personalized treatment plans for cardiac patients leveraging AI.
Increased trust in AI-driven medical tools could accelerate their integration into broader clinical workflows, reducing diagnostic errors and burdens on human clinicians.
The success of such frameworks in cardiac care could set a precedent for developing similar resilient AI solutions across other medical specialties or data-poor critical infrastructure domains.
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Read at arXiv cs.LG