A Hadoop‐big data analytic model to predict and classify chronic kidney diseases using improved fractional rough fuzzy K‐means clustering and extreme gradient boost rat swarm optimizer. (4th October 2022)

• Record Type: Journal Article
• Authors: Vijayalakshmi, J.; Ramaraj, E.
• Is Part Of: Concurrency and computation. Volume 34:Number 28(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-10-04
• Subjects: big data -- chronic kidney diseases -- improved fractional rough K means clustering -- rat swarm optimization -- XG boost classifier; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.7354
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: In this article, a Hadoop‐big data based chronic kidney disease prediction and classification using improved fractional rough fuzzy K‐means (IF‐RFKM) clustering and XG boost rat swarm optimizer is proposed. Here, IF‐RFKM clustering method is contemplated for the disease prediction. This disease is classified using XG boost classifier for classifying the stages of chronic kidney diseases as normal and abnormal. Moreover, the rat swarm optimization (RSO) algorithm is proposed for optimizing the parameters of the XG boost classifier. Initially, the data is randomly generated from CKD dataset. The simulation is carried out in python language. From the simulation, the proposed method attains higher accuracy 99.57%, 98.28%, and 97.35%, higher recall 98.23%, 88.34%, and 78.96% and lower execution time 92.15%, 90.25%, and 92.48% compared with existing methods, like chronic kidney disease detection and classification by recursive feature elimination using decision tree (CCKD‐RFE‐DT). An efficient chronic kidney disease classification and clustering using logistic regression (CCKD‐LR) and efficient chronic kidney disease classification utilizing multi‐kernel support vector machine with (CCKD‐MKC‐SVM).