Classification of Chronic Kidney Disease with Most Known Data Mining Methods
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.