Open AccessRecursive Particle Swarm Optimization (RPSO) schemed Support Vector Machine (SVM) Implementation for Microarray Data Analysis on Chronic Kidney Disease (CKD)
Author(s) -
Zuherman Rustam,
Mas Andam Syarifah,
Titin Siswantining
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/546/5/052077
Subject(s) - particle swarm optimization , support vector machine , kidney disease , microarray analysis techniques , microarray , gene chip analysis , computer science , data mining , medicine , artificial intelligence , machine learning , gene expression , gene , biology , biochemistry
Chronic Kidney Disease is the second chronical and catastrophic disease after heart disease in terms of treatment cost. This is because CKD symptoms occurs on final stages, that is fourth and fifth, in which it is too late for treatment. Therefore, final stage patient must receive continuous medication, such as haemodialysis. So early detection on a patient CKD is necessary to prevent patient to be chronic. Studies of gene genes are used to classify microarray data with global CKD decisions or not. So to get accurate results in this study using SVM-RFE with the addition of the Particle Swarm Optimization algorithm as a gene selector to be more optimal and it consideration of the fixed gene in its condition which is important information of the CKD gene itself. This research is then expected to be able to classify globally with CKD output or not CKD. As a result, for the CKD microarray data accuracy using RPSO schemed SVM highest than only using SVM-RFE.