Quantifying the imputation paradox and XAI inconsistency in multi-source diabetes prediction: a 353,680-record leakage-free stacking ensemble with dynamic routing architecture.
- DOI
- 10.3389/frai.2026.1898153
- Published
- 2026
- Container
- Frontiers in artificial intelligence
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium: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.},
author = {Nanda Kishore M and Navaneethan C},
year = {2026},
journal = {Frontiers in artificial intelligence},
doi = {10.3389/frai.2026.1898153},
url = {https://doi.org/10.3389/frai.2026.1898153}
}RIS
TY - JOUR TI - Quantifying the imputation paradox and XAI inconsistency in multi-source diabetes prediction: a 353,680-record leakage-free stacking ensemble with dynamic routing architecture. AU - Nanda Kishore M AU - Navaneethan C PY - 2026 JO - Frontiers in artificial intelligence DO - 10.3389/frai.2026.1898153 UR - https://doi.org/10.3389/frai.2026.1898153 ER -
APA
M, N. K., & C, N. (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.. Frontiers in artificial intelligence. https://doi.org/10.3389/frai.2026.1898153
Source records
- pubmed · retrieved 2026-09-25T13:25:41.475Z