Fahad Alroumi, Raquel Belforti, Nadia Villarroel, Rebecca D Blanchard
Curating a media-linked curriculum
The clinical teacher
clinical decisionmaking chronic disorders like chronic kidney disease high variability often leads uncertainty negative outcomes deep learning techniques developed useful tools minimizing chance improving clinical decisionmaking moreover traditional techniques chronic kidney disease recognition frequently accuracy compromised relies limited sets biological attributes therefore proposed work combination deep radial bias network puma optimization algorithm suggested precised chronic kidney disease classification initially accessed data undergo preprocessing using spectral z score bag boost kmeans smote transformation includes robust scaling data cleaning balancing encoding handling missing values minmax scaling zstandardization feature selection conducted using hybrid methodology roleoriented binary walrus grey wolf algorithm choose discriminative features improving classification accuracy auto encoder patchbased principal component analysis employed dimensionality reduction minimize processing time finally proposed classification method utilizes deep radial bias puma optimization search algorithm effective chronic kidney disease classification introduced scheme tested two datasets risk factor prediction chronic kidney disease dataset chronic kidney disease dataset provides accuracies 9902 9915 respectively experiments demonstrate proposed model identifies chronic kidney disease accurately existing approaches