Author:
Rubini L. Jerlin,Perumal Eswaran
Abstract
In the present day, distributed algorithms become more popular due to their diversity in several applications. The prediction and reorganization of medical data required more practice and information. We propose a novel approach feature selection based on efficient chronic kidney disease
(CKD) prediction and classification. Primarily, the pre-processing pace will be implemented over the input data. Then, the grey wolf optimization (GWO) algorithm gets executed to choose the optimal features from the pre-processed data. Next, the projected technique exploits the Hybrid Kernel
Support Vector Machine (HKSVM) as a classification model to identify the presence of CKD or not. The simulation takes place in MATLAB. The validation of the presented model takes place using a benchmark CKD dataset as of machine learning repository such as UCI under the presence of several
measures. New outcome specified that the planned categorization arrangement has surpassed by containing enhanced 97.26% accuracy for kidney chronic dataset when contrasted with existing SVM technique only accomplished 94.77% and fuzzy min–max GSO neural network (FMMGNN) classifier accomplished
93.78%.
Publisher
American Scientific Publishers
Subject
Health Informatics,Radiology Nuclear Medicine and imaging
Cited by
11 articles.
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