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

doi:10.1109/GRC.2014.6982828

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 proceeding › Conference contribution

1 Citation (Scopus)

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 language	English
Title of host publication	Proceedings - 2014 IEEE International Conference on Granular Computing, GrC 2014
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	166-169
Number of pages	4
ISBN (Print)	9781479954643
DOIs	https://doi.org/10.1109/GRC.2014.6982828
Publication status	Published - Dec 11 2014
Externally published	Yes
Event	2014 IEEE International Conference on Granular Computing, GrC 2014 - Hokkaido, Japan Duration: Oct 22 2014 → Oct 24 2014

Other

Other	2014 IEEE International Conference on Granular Computing, GrC 2014
Country/Territory	Japan
City	Hokkaido
Period	10/22/14 → 10/24/14

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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10.1109/GRC.2014.6982828

Cite this

Improvement of prognostic models for ESRD mortality by the bootstrap method with random hot deck imputation. / Lin, Ting Ru; Yang, Ching Jung; Chiang, I. Jen.

Proceedings - 2014 IEEE International Conference on Granular Computing, GrC 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 166-169 6982828.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Lin, TR, Yang, CJ & Chiang, IJ 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., 6982828, Institute of Electrical and Electronics Engineers Inc., pp. 166-169, 2014 IEEE International Conference on Granular Computing, GrC 2014, Hokkaido, Japan, 10/22/14. https://doi.org/10.1109/GRC.2014.6982828

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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",

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

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Keywords

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