CLASSIFICATION OF KIDNEY DISEASE USING GENETIC MODIFIED KNN AND ARTIFICIAL BEE COLONY ALGORITHM

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Field Value
 
Title CLASSIFICATION OF KIDNEY DISEASE USING GENETIC MODIFIED KNN AND ARTIFICIAL BEE COLONY ALGORITHM
 
Creator Ariani, Ardina
Samsuryadi, Samsuryadi
 
Subject
Artificial Bee Colony; Classification of Kidney Disease; Feature Selection; Genetic Modified K-Nearest Neighbor;
 
Description The health care system is currently improving with the development of intelligent artificial systems in detecting diseases. Early detection of kidney disease is essential by recognizing symptoms to prevent more severe damages. This study introduces a classification system for kidney diseases using the Artificial Bee Colony (ABC) algorithm and genetically modified K-Nearest Neighbor (KNN). ABC algorithm is used as a feature selection to determine relevant symptoms used in influencing kidney disease and Genetic modified KNN used for classification. This research consists of 3 stages: pre-processing, feature selection, and classification. However, it focuses on the pre-processing stage of chronic kidney disease using 400 records with 24 attributes for the feature selection and classification. Kidney disease data is classified into two classes, namely chronic kidney disease and not chronic kidney disease. Furthermore, the performance of the proposed method is compared with other methods. The result showed that an accuracy of 98.27% was obtained by dividing the dataset into 280 training and 120 test data.
 
Publisher Universitas Mercu Buana
 
Contributor
 
Date 2021-02-20
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier https://publikasi.mercubuana.ac.id/index.php/sinergi/article/view/8701
10.22441/sinergi.2021.2.009
 
Source SINERGI; Vol 25, No 2 (2021); 177-184
24601217
14102331
 
Language eng
 
Relation https://publikasi.mercubuana.ac.id/index.php/sinergi/article/view/8701/4181
https://publikasi.mercubuana.ac.id/index.php/sinergi/article/downloadSuppFile/8701/1316
https://publikasi.mercubuana.ac.id/index.php/sinergi/article/downloadSuppFile/8701/1326
 
Rights Copyright (c) 2021 SINERGI
 

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