Classification of Chronic Kidney Disease (CKD) Using Rule based Classifier and PCA

Aung Nway Oo

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Classification of Chronic Kidney Disease (CKD) Using Rule based Classifier and PCA

http://hdl.handle.net/20.500.12678/0000006173

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Publication type
		Journal article
Upload type
		Publication
Title
	Title	Classification of Chronic Kidney Disease (CKD) Using Rule based Classifier and PCA
	Language	en
Publication date		2018-04-02
Authors
		Aung Nway Oo
Description
		Feature selection is a process which attempts to minimize the problems caused by high feature dimensionalities. This is normally achieved in feature extraction step in data mining. The main task of feature extraction is to select or combine the features that preserve most of the information and remove the redundant components in order to improve the efficiency of the subsequent classifiers without degrading their performances. For feature selection, Principal Component Analysis (PCA) is used in this paper. PCA is the one of the most popular methods for feature selection. The rule based approach is most useful in the classification problem. In this paper, rule based classification algorithms, namely PART, RIDOR and JRIP are used for classification of Chronic Kidney Disease (CKD) dataset. The classification results of normal rule based algorithm and feature selection based classification results are compared and analyzed.
Keywords
		Feature selection, Rule based Classifier, PCA, PART, RIDOR, JRIP, CKD
Journal articles
		International Journal of Management, Technology And Engineering (IJMTE)
		728-732