Improvement of prognostic models for ESRD mortality by the bootstrap method with random hot deck imputation

Ting Ru Lin, Ching Jung Yang, I. Jen Chiang

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

Abstract

Prognostic models for end-stage renal disease (ESRD) have been researched extensively as an increasing prevalence internationally. Different machine learning and statistic algorithms for the models were proposed in studies corresponding to different medical datasets including a quantity of missing values for optimal outcomes. We approached this issue by applying stepwise logistic regression, ANN, and SVM algorithms to an ESRD dataset after case deletion and calculated areas under ROC curves of three algorithms as comparisons, resulting in 0.757, 0.664 and 0.704, respectively. The random hot deck, oversampling, and bootstrap methods were employed in data preprocessing to compensate the minor mortality. Afterward, average AUC of three algorithms approximated 0.90 (p

Original languageEnglish
Title of host publicationProceedings - 2014 IEEE International Conference on Granular Computing, GrC 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages166-169
Number of pages4
ISBN (Print)9781479954643
DOIs
Publication statusPublished - Dec 11 2014
Externally publishedYes
Event2014 IEEE International Conference on Granular Computing, GrC 2014 - Hokkaido, Japan
Duration: Oct 22 2014Oct 24 2014

Other

Other2014 IEEE International Conference on Granular Computing, GrC 2014
CountryJapan
CityHokkaido
Period10/22/1410/24/14

Keywords

  • ANN
  • end-stage renal diseases
  • oversampling
  • random hot deck imputation
  • stepwise logistic regression
  • SVM
  • the bootstrap method

ASJC Scopus subject areas

  • Computer Science Applications
  • Software

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  • Cite this

    Lin, T. R., Yang, C. J., & Chiang, I. J. (2014). Improvement of prognostic models for ESRD mortality by the bootstrap method with random hot deck imputation. In Proceedings - 2014 IEEE International Conference on Granular Computing, GrC 2014 (pp. 166-169). [6982828] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/GRC.2014.6982828