| RESEARCH ARTICLE |
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| Year : 2017 | Volume
: 8
| Issue : 1 | Page : 24 |
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A reduced set of features for chronic kidney disease prediction
Rajesh Misir1, Malay Mitra2, Ranjit Kumar Samanta2
1 Department of Computer Science, Vidyasagar University, Medinipur, India
2 Department of Computer Science and Application, Expert Systems Laboratory, University of North Bengal, Darjeeling, West Bengal, India
Correspondence Address:
Ranjit Kumar Samanta
Department of Computer Science and Application, Expert Systems Laboratory, University of North Bengal, Darjeeling - 734 013, West Bengal
India
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/jpi.jpi_88_16
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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. |
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