DOIONLINE 
DOIONLINE Publish In |
International Journal of Advances in Science, Engineering and Technology(IJASEAT)-IJASEAT |
 Journal Home Volume Issue |
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Issue |
Volume-5,Issue-1, Spl. Iss-2 ( Feb, 2017 ) | |||||||||
Paper Title |
Classification of Chronic Kidney Disease with Most Known Data Mining Methods | |||||||||
Author Name |
Murat Koklu, Kemal Tutuncu | |||||||||
Affilition |
Department of Computer Engineering, Selcuk University, Konya, TURKEY Department of Electrical – Electronics Engineering, Selcuk University, Konya, TURKEY | |||||||||
Pages |
14-18 | |||||||||
Abstract |
Data mining, a step of knowledge discovery process, has gathered together statistical, database, machine learning and artificial intelligence studies in recent researches. When investigating large amounts of data, it is important to use an effective search method for the occurrence of patterns. Statistical and machine learning techniques are used for the determination of the models to be used for data mining predictions. Today, Data mining is used in many different areas such as science and engineering, health, commerce, shopping, banking and finance, education and internet.The objective of this study is Chronic kidney disease dataset using 4 different Data Mining methods namely; Naive Bayes, C4.5 Algorithm, Support Vector Machine (SVM) and Multilayer Perceptron. Correctly classified instances were found as 95,00%, 97,75%, 99,00% and 99,75% for Naive Bayes, C4.5 Algorithm, SVM and Multilayer Perceptron respectively. Keywords- Chronic Kidney Disease, Data Mining, Naive Bayes, C4.5,SVM, Algorithm, Multilayer Perceptron. | |||||||||
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