AccScience Publishing / AIH / Online First / DOI: 10.36922/AIH026330112
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ORIGINAL RESEARCH ARTICLE

Cooperative metaheuristic feature selection and out-of-fold weighted ensemble learning for chronic kidney disease prediction from a reduced attribute panel

Amaresh Ayyanahalli Math1* ,  Ananda Murthy Meenakshi Sundaram1*
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1 School of Computer Science and Engineering, Faculty of Engineering and Technology, REVA University, Bangalore, Karnataka , India
AIH, 026330112 https://doi.org/10.36922/AIH026330112
Received: 11 August 2026 | Revised: 1 September 2026 | Accepted: 21 September 2026 | Published online: 13 October 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Chronic kidney disease is diagnosed from a broad laboratory panel whose size and composition are open to optimization. This study pairs a cooperative hybrid metaheuristic for attribute selection with a weighted voting ensemble. Genetic, swarm, and annealing sub-populations evolved in parallel over one shared fitness landscape, exchanged elites every five iterations, and were refined by annealing applied as a local search on the incumbent; the attributes retained across the elite band formed the subset, which fed four heterogeneous learners whose probabilities were fused using weights tuned from out-of-fold predictions only. Imputation, search, learners, and weights were confined to the training partition of a 10 times five-fold cross-validation, and every single-operator variant, four classical selectors, and the unreduced panel were evaluated under that identical protocol. The ensemble reached 99.00% accuracy, an area under the curve of 0.9994, and an F-measure of 99.19% from 11 of the 24 attributes, dropping 53% of the assays at a cost of two-thirds of a percentage point that is not statistically significant once the dependence between cross-validation folds is modeled. Accuracy held at 98.00% when a further fifth of all values was deleted, and an independent cohort of 200 records recorded as discretized intervals returned 98.00%. The pipeline is statistically equivalent to the strongest single learner and to the strongest classical selector while using fewer than half the attributes.

Graphical abstract
Keywords
Chronic kidney disease
Hybrid metaheuristic optimization
Feature selection
Ensemble classification
Nested validation
Reproducibility
External validation
Funding
None.
Conflict of interest
The authors declare they have no competing interests.
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Artificial Intelligence in Health, Electronic ISSN: 3029-2387 Print ISSN: 3041-0894, Published by AccScience Publishing
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