Attribute Selection in Naive Bayes Algorithm Using Genetic Algorithms and Bagging for Prediction of Liver Disease
| Main Authors: | Utami, Dwi Yuni, Nurlelah, Elah, Hikmah, Noer |
|---|---|
| Format: | Article info application/pdf eJournal |
| Bahasa: | eng |
| Terbitan: |
Universitas Medan Area
, 2020
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| Online Access: |
http://www.ojs.uma.ac.id/index.php/jite/article/view/3793 http://www.ojs.uma.ac.id/index.php/jite/article/view/3793/2772 |
| ctrlnum |
article-3793 |
|---|---|
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<dc schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><title lang="en-US">Attribute Selection in Naive Bayes Algorithm Using Genetic Algorithms and Bagging for Prediction of Liver Disease</title><creator>Utami, Dwi Yuni</creator><creator>Nurlelah, Elah</creator><creator>Hikmah, Noer</creator><subject lang="en-US"/><subject lang="en-US"/><description lang="en-US">Liver disease is an inflammatory disease of the liver and can cause the liver to be unable to function as usual and even cause death. According to WHO (World Health Organization) data, almost 1.2 million people per year, especially in Southeast Asia and Africa, have died from liver disease. The problem that usually occurs is the difficulty of recognizing liver disease early on, even when the disease has spread. This study aims to compare and evaluate Naive Bayes algorithm as a selected algorithm and Naive Bayes algorithm based on Genetic Algorithm (GA) and Bagging to find out which algorithm has a higher accuracy in predicting liver disease by processing a dataset taken from the UCI Machine Learning Repository database (GA). University of California Invene). From the results of testing by evaluating both the confusion matrix and the ROC curve, it was proven that the testing carried out by the Naive Bayes Optimization algorithm using Algortima Genetics and Bagging has a higher accuracy value than only using the Naive Bayes algorithm. The accuracy value for the Naive Bayes algorithm model is 66.66% and the accuracy value for the Naive Bayes model with attribute selection using Genetic Algorithms and Bagging is 72.02%. Based on this value, the difference in accuracy is 5.36%.Keywords: Liver Disease, Naïve Bayes, Genetic Agorithms, Bagging.</description><publisher lang="en-US">Universitas Medan Area</publisher><contributor lang="en-US"/><date>2020-07-20</date><type>Journal:Article</type><type>Other:info:eu-repo/semantics/publishedVersion</type><type>Journal:Article</type><type>File:application/pdf</type><identifier>http://www.ojs.uma.ac.id/index.php/jite/article/view/3793</identifier><identifier>10.31289/jite.v4i1.3793</identifier><source lang="en-US">JITE (JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING); Vol 4, No 1 (2020): ---> EDISI JULI; 76-85</source><source>2549-6255</source><source>2549-6247</source><source>10.31289/jite.v4i1</source><language>eng</language><relation>http://www.ojs.uma.ac.id/index.php/jite/article/view/3793/2772</relation><rights lang="en-US">Copyright (c) 2020 JITE (JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING)</rights><rights lang="en-US">http://creativecommons.org/licenses/by-nc/4.0</rights><recordID>article-3793</recordID></dc>
|
| language |
eng |
| format |
Journal:Article Journal Other:info:eu-repo/semantics/publishedVersion Other File:application/pdf File Journal:eJournal |
| author |
Utami, Dwi Yuni Nurlelah, Elah Hikmah, Noer |
| title |
Attribute Selection in Naive Bayes Algorithm Using Genetic Algorithms and Bagging for Prediction of Liver Disease |
| publisher |
Universitas Medan Area |
| publishDate |
2020 |
| url |
http://www.ojs.uma.ac.id/index.php/jite/article/view/3793 http://www.ojs.uma.ac.id/index.php/jite/article/view/3793/2772 |
| contents |
Liver disease is an inflammatory disease of the liver and can cause the liver to be unable to function as usual and even cause death. According to WHO (World Health Organization) data, almost 1.2 million people per year, especially in Southeast Asia and Africa, have died from liver disease. The problem that usually occurs is the difficulty of recognizing liver disease early on, even when the disease has spread. This study aims to compare and evaluate Naive Bayes algorithm as a selected algorithm and Naive Bayes algorithm based on Genetic Algorithm (GA) and Bagging to find out which algorithm has a higher accuracy in predicting liver disease by processing a dataset taken from the UCI Machine Learning Repository database (GA). University of California Invene). From the results of testing by evaluating both the confusion matrix and the ROC curve, it was proven that the testing carried out by the Naive Bayes Optimization algorithm using Algortima Genetics and Bagging has a higher accuracy value than only using the Naive Bayes algorithm. The accuracy value for the Naive Bayes algorithm model is 66.66% and the accuracy value for the Naive Bayes model with attribute selection using Genetic Algorithms and Bagging is 72.02%. Based on this value, the difference in accuracy is 5.36%.Keywords: Liver Disease, Naïve Bayes, Genetic Agorithms, Bagging. |
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Universitas Medan Area |
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Data Processing, Computer Science/Pemrosesan Data, Ilmu Komputer, Teknik Informatika Computer Modeling and Simulation/Model dan Simulasi Komputer |
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KOTA MEDAN |
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SUMATERA UTARA |
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