Detection of Chronic Kidney Disease and Selecting Important Predictive Attributes

Asif Salekin, John Stankovic

Research output: Chapter in Book/Report/Conference proceedingConference contribution

39 Scopus citations

Abstract

Chronic kidney disease (CKD) is a major public health concern with rising prevalence. In this study we consider 24 predictive parameters and create a machine learning classifier to detect CKD. We evaluate our approach on a dataset of 400 individuals, where 250 of them have CKD. Using our approach we achieve a detection accuracy of 0.993 according to the F1-measure with 0.1084 root mean square error. This is a 56% reduction of mean square error compared to the state of the art (i.e., the CKD-EPI equation: a glomerular filtration rate estimator). We also perform feature selection to determine the most relevant attributes for detecting CKD and rank them according to their predictability. We identify new predictive attributes which have not been used by any previous GFR estimator equations. Finally, we perform a cost-accuracy tradeoff analysis to identify a new CKD detection approach with high accuracy and low cost.

Original languageEnglish (US)
Title of host publicationProceedings - 2016 IEEE International Conference on Healthcare Informatics, ICHI 2016
EditorsWai-Tat Fu, Kai Zheng, Larry Hodges, Gregor Stiglic, Ann Blandford
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages262-270
Number of pages9
ISBN (Electronic)9781509061174
DOIs
StatePublished - Dec 6 2016
Externally publishedYes
Event2016 IEEE International Conference on Healthcare Informatics, ICHI 2016 - Chicago, United States
Duration: Oct 4 2016Oct 7 2016

Publication series

NameProceedings - 2016 IEEE International Conference on Healthcare Informatics, ICHI 2016

Conference

Conference2016 IEEE International Conference on Healthcare Informatics, ICHI 2016
CountryUnited States
CityChicago
Period10/4/1610/7/16

Keywords

  • Chronic kidney disease
  • feature selection
  • machine learning

ASJC Scopus subject areas

  • Health Informatics
  • Health(social science)
  • Computer Networks and Communications
  • Computer Science Applications

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  • Cite this

    Salekin, A., & Stankovic, J. (2016). Detection of Chronic Kidney Disease and Selecting Important Predictive Attributes. In W-T. Fu, K. Zheng, L. Hodges, G. Stiglic, & A. Blandford (Eds.), Proceedings - 2016 IEEE International Conference on Healthcare Informatics, ICHI 2016 (pp. 262-270). [7776352] (Proceedings - 2016 IEEE International Conference on Healthcare Informatics, ICHI 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICHI.2016.36