An effective role-oriented binary Walrus Grey Wolf approach for feature selection in early-stage chronic kidney disease detection.

Abstract

In clinical decision-making for chronic disorders like chronic kidney disease, high variability often leads to uncertainty and negative outcomes. Deep learning techniques have been developed as useful tools for minimizing the chance and improving clinical decision-making. Moreover, traditional techniques for chronic kidney disease recognition frequently the accuracy is compromised as it relies on limited sets of biological attributes. Therefore, in the proposed work, a combination of deep radial bias network and the puma optimization algorithm is suggested for precised chronic kidney disease classification. Initially, the accessed data undergo preprocessing using Spectral Z score Bag Boost K-Means SMOTE transformation, which includes robust scaling, data cleaning, balancing, encoding, handling missing values, min-max scaling, and z-standardization. Feature selection is then conducted using the hybrid methodology of Role-oriented Binary Walrus Grey Wolf Algorithm to choose discriminative features for improving classification accuracy. Then, Auto Encoder with Patch-Based Principal Component Analysis is employed for dimensionality reduction to minimize the processing time. Finally, the proposed classification method utilizes deep radial bias and the puma optimization search algorithm for effective chronic kidney disease classification. The introduced scheme is tested on two datasets: the risk factor prediction of chronic kidney disease dataset and chronic kidney disease dataset, which provides accuracies of 99.02%, and 99.15%, respectively. Experiments demonstrate that the proposed model identifies chronic kidney disease more accurately than the existing approaches.