Target leakage, not model class, explains reported accuracy in survey-based cardiovascular screening: a leakage-tiered audit of glass-box and tabular foundation models

Abstract

Cardiovascular screening models trained on national health surveys routinely report areas under the receiver operating characteristic curve (AUROC) near 0.89. We asked whether that accuracy reflects learning or target leakage, whether tabular foundation models change the answer, and whether the properties deployment requires survive joint examination. We benchmarked ten classifiers spanning linear, tree-ensemble, neural, glass-box, and tabular foundation classes for prevalent myocardial infarcti

Categories

@misc{tareaf2026target,
  title = {{Target leakage, not model class, explains reported accuracy in survey-based cardiovascular screening: a leakage-tiered audit of glass-box and tabular foundation models}},
  author = {Raad Bin Tareaf and Murad Al-Rajab and Samia Loucif and Samer Ellaham and Cedric Schmitz},
  year = {2026},
  month = sep,
  eprint = {2609.11838},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.11838}
}