a reduced set of features for chronic kidney disease prediction

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ID: 189270
2017
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Abstract
Chronic kidney disease (CKD) is one of the life-threatening diseases. Early detection and proper management are solicited for augmenting survivability. As per the UCI data set, there are 24 attributes for predicting CKD or non-CKD. At least there are 16 attributes need pathological investigations involving more resources, money, time, and uncertainties. The objective of this work is to explore whether we can predict CKD or non-CKD with reasonable accuracy using less number of features. An intelligent system development approach has been used in this study. We attempted one important feature selection technique to discover reduced features that explain the data set much better. Two intelligent binary classification techniques have been adopted for the validity of the reduced feature set. Performances were evaluated in terms of four important classification evaluation parameters. As suggested from our results, we may more concentrate on those reduced features for identifying CKD and thereby reduces uncertainty, saves time, and reduces costs.
Reference Key
misir2017journala Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Rajesh Misir;Malay Mitra;Ranjit Kumar Samanta
Journal annual review of materials science
Year 2017
DOI
10.4103/jpi.jpi_88_16
URL
http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2017;volume=8;issue=1;spage=24;epage=24;aulast=Misir
Keywords
chronic kidney disease correlation medical informaticspathology

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