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

研究成果: 書貢獻/報告類型 › 會議貢獻

1 引文斯高帕斯（Scopus）

摘要

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

原文	英語
主出版物標題	Proceedings - 2014 IEEE International Conference on Granular Computing, GrC 2014
發行者	Institute of Electrical and Electronics Engineers Inc.
頁面	166-169
頁數	4
ISBN（列印）	9781479954643
DOIs	https://doi.org/10.1109/GRC.2014.6982828
出版狀態	已發佈 - 12月 11 2014
對外發佈	是
事件	2014 IEEE International Conference on Granular Computing, GrC 2014 - Hokkaido, 日本持續時間: 10月 22 2014 → 10月 24 2014

其他

其他	2014 IEEE International Conference on Granular Computing, GrC 2014
國家/地區	日本
城市	Hokkaido
期間	10/22/14 → 10/24/14

ASJC Scopus subject areas

電腦科學應用
軟體

存取文件

10.1109/GRC.2014.6982828

其它文件與鏈接

引用此

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.

研究成果: 書貢獻/報告類型 › 會議貢獻

Lin, TR, Yang, CJ & Chiang, IJ 2014, Improvement of prognostic models for ESRD mortality by the bootstrap method with random hot deck imputation. 於 Proceedings - 2014 IEEE International Conference on Granular Computing, GrC 2014., 6982828, Institute of Electrical and Electronics Engineers Inc., 頁 166-169, 2014 IEEE International Conference on Granular Computing, GrC 2014, Hokkaido, 日本, 10/22/14. https://doi.org/10.1109/GRC.2014.6982828

@inproceedings{19e01a4f11e048be8bb33b57777b2443,

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

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",

keywords = "ANN, end-stage renal diseases, oversampling, random hot deck imputation, stepwise logistic regression, SVM, the bootstrap method",

author = "Lin, {Ting Ru} and Yang, {Ching Jung} and Chiang, {I. Jen}",

year = "2014",

month = dec,

day = "11",

doi = "10.1109/GRC.2014.6982828",

language = "English",

isbn = "9781479954643",

pages = "166--169",

booktitle = "Proceedings - 2014 IEEE International Conference on Granular Computing, GrC 2014",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "2014 IEEE International Conference on Granular Computing, GrC 2014 ; Conference date: 22-10-2014 Through 24-10-2014",

}

TY - GEN

T1 - Improvement of prognostic models for ESRD mortality by the bootstrap method with random hot deck imputation

AU - Lin, Ting Ru

AU - Yang, Ching Jung

AU - Chiang, I. Jen

PY - 2014/12/11

Y1 - 2014/12/11

N2 - 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

AB - 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

KW - ANN

KW - end-stage renal diseases

KW - oversampling

KW - random hot deck imputation

KW - stepwise logistic regression

KW - SVM

KW - the bootstrap method

UR - http://www.scopus.com/inward/record.url?scp=84920723207&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84920723207&partnerID=8YFLogxK

U2 - 10.1109/GRC.2014.6982828

DO - 10.1109/GRC.2014.6982828

M3 - Conference contribution

AN - SCOPUS:84920723207

SN - 9781479954643

SP - 166

EP - 169

BT - Proceedings - 2014 IEEE International Conference on Granular Computing, GrC 2014

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2014 IEEE International Conference on Granular Computing, GrC 2014

Y2 - 22 October 2014 through 24 October 2014

ER -

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

摘要

其他

ASJC Scopus subject areas

存取文件

其它文件與鏈接

指紋

引用此