Enhanced early chronic kidney disease prediction using hybrid waterwheel plant algorithm for deep neural network optimization

Chronic Kidney Disease (CKD) is a progressive condition primarily caused by diabetes and hypertension, affecting millions worldwide. Early diagnosis remains a clinical challenge since traditional approaches, such as Glomerular Filtration Rate (GFR) estimation and kidney damage indicators, often fail...

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Published in:Scientific reports Vol. 15; no. 1; pp. 42584 - 23
Main Authors: Khafaga, Doaa Sami, Khodadadi, Nima, Khodadadi, Ehsaneh, Ali Alhussan, Amel, Eid, Marwa M, El-Kenawy, El-Sayed M
Format: Journal Article
Language:English
Published: England Nature Publishing Group 27.11.2025
Nature Portfolio
Subjects:
Algorithms
Biomarkers
Chronic kidney disease prediction
Deep Learning
Deep neural networks
Diabetes mellitus
Early Diagnosis
Early medical diagnosis
Glomerular Filtration Rate
Grey wolf optimization
Humans
Hybrid optimization algorithms
Hypertension
Kidney diseases
Kidneys
Mortality
Neural networks
Neural Networks, Computer
Renal Insufficiency, Chronic - diagnosis
Sensory integration
Variance analysis
Waterwheel plant algorithm
Early medical diagnosis
Grey wolf optimization
Deep neural networks
Hybrid optimization algorithms
Chronic kidney disease prediction
Waterwheel plant algorithm
ISSN:2045-2322, 2045-2322
Online Access:Get full text
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Summary:Chronic Kidney Disease (CKD) is a progressive condition primarily caused by diabetes and hypertension, affecting millions worldwide. Early diagnosis remains a clinical challenge since traditional approaches, such as Glomerular Filtration Rate (GFR) estimation and kidney damage indicators, often fail to detect CKD in its initial stages. This study aims to enhance early CKD prediction by developing a deep neural network optimized with a novel hybrid metaheuristic that combines the Waterwheel Plant Algorithm (WWPA) with Grey Wolf Optimization (GWO). Using the UCI CKD dataset, rigorous preprocessing techniques-including data imputation, normalization, and synthetic oversampling-were employed to enhance data quality and mitigate class imbalance. A multilayer perceptron (MLP) regression model was trained and optimized through the WWPA-GWO framework and benchmarked against other optimization algorithms, including PSO, GA, and WOA. Results demonstrated that the standard MLP achieved moderate performance (MSE = 0.00177, RMSE = 0.0420, MAE = 0.0100, [Formula: see text] = 0.8793), whereas the optimized model achieved significant improvements (MSE = [Formula: see text], RMSE = 0.00175, [Formula: see text] = 0.9730) with reduced computational time (0.0999 s). Statistical validation using ANOVA ([Formula: see text]) and Wilcoxon signed-rank testing ([Formula: see text]) confirmed the robustness of the approach. These findings highlight the effectiveness of the WWPA-GWO hybrid optimization strategy for deep neural networks, offering a reliable and efficient pathway for early CKD detection. Future work will explore the integration of advanced imputation methods, multi-modal data sources, and federated learning frameworks to enhance the model's generalizability and clinical utility in diverse healthcare settings.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-26382-6