{
  "id": 17495,
  "url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1898153",
  "title": "Quantifying the imputation paradox and XAI inconsistency in multi-source diabetes prediction: a 353,680-record leakage-free stacking ensemble with dynamic routing architecture",
  "summary": "Diabetes affects 537 million adults globally, a figure projected to reach 783 million by 2045. Despite over 4,200 ML prediction studies, clinical translation remains hindered by an over-reliance on benchmark datasets, unmeasured information costs of multi-source fusion, and untested XAI convergence assumptions. We address these issues by evaluating 353,680 records (from 455,446 candidates) across a Clinical-Biomarker Set (CBS) and a Lifestyle-Survey Set (LSS) using a strict leakage-free protocol",
  "authors": "M. Nanda Kishore",
  "category": "research",
  "topics": "healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-07T00:00:00.000Z",
  "fetched_at": "2026-08-08T05:10:34.355Z",
  "source_slug": "x-frontiers-in-artificial-intelligence",
  "source_name": "Frontiers in Artificial Intelligence",
  "source_homepage": "https://www.frontiersin.org/journals/artificial-intelligence",
  "ethics_ai_record_url": "https://ethics.ai/record/17495",
  "original_url": "https://www.frontiersin.org/articles/10.3389/frai.2026.1898153",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}