Quantifying the imputation paradox and XAI inconsistency in multi-source diabetes prediction: a 353,680-record leakage-free stacking ensemble with dynamic routing architecture
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
Record details
Published: 7 August 2026
Source: Frontiers in Artificial Intelligence
Category: Research
Topics: Healthcare
Retrieved: 8 August 2026
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How to cite this record
ethics.ai (7 August 2026), “Quantifying the imputation paradox and XAI inconsistency in multi-source diabetes prediction: a 353,680-record leakage-free stacking ensemble with dynamic routing architecture,” evidence record 17495, https://ethics.ai/record/17495 (originally published by Frontiers in Artificial Intelligence).
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