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

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.