Hybrid Metaheuristic Optimization with Stacked Sparse Autoencoder for Enhanced Chronic Kidney Disease Detection and Classification
DOI:
https://doi.org/10.24237/djes.2026.19211Keywords:
Chronic Kidney Disease, Dwarf Mongoose Optimizer, Giant Trevally Optimizer, RMSProp, Metaheuristic Optimization, Medical DiagnosisAbstract
Chronic Kidney Disease (CKD) is a progressive renal disease that needs to be accurately and early diagnosed to eliminate irreversible damage to the kidney. Nevertheless, traditional methods of diagnosis might not be particularly effective at capturing the complex nonlinear patterns between clinical attributes. The paper offers a Hybrid Metaheuristic Optimization with Stacked Sparse Autoencoder framework as a superior method of CKD detection and classification. The suggested model is based on the combination of Z-score normalization, hybrid Dwarf Mongese Optimizer-Giant Trevall Optimizer based feature selection, deep hierarchical feature representation with Stacked Sparse Autoencoder, and RMSProp-based adaptive optimization. The DMO aspect facilitates exploration of the needed clinical properties globally whereas GTO enhances local optimization of the chosen set of features. The SSAE also trains small sparse representations to minimize redundancy and enhance the interpretability of the diagnoses. The framework was tested on the UCI CKD dataset of 400 patient records having 24 clinical attributes. The proposed hybrid optimization model achieved an accuracy of 99.25%, precision of 99.10%, recall of 99.40%, specificity of 99.05%, F1-score of 99.25%, MCC of 0.985, AUC of 0.997, and an error rate of 0.75%. The proposed model had a higher accuracy of 1.75% and 0.85 when compared with XGBoost+BSO and Two-Tier ACBPNN, respectively. These findings validate the hypothesis that hybrid feature-selection and sparse deep-representation enhance the diagnostic reliability, misclassification, and enable interpretable CKD screening. The suggested framework has a high potential to be a clinical decision-support model of early CKD detection.
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