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
N1 - Publisher Copyright:
© 2014 IEEE.
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<0.02, unpaired t-test). As a result, the mentioned strategies dealing with bias medical data may ameliorate prognostic ESRD models in clinic.
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<0.02, unpaired t-test). As a result, the mentioned strategies dealing with bias medical data may ameliorate prognostic ESRD models in clinic.
KW - ANN
KW - SVM
KW - end-stage renal diseases
KW - oversampling
KW - random hot deck imputation
KW - stepwise logistic regression
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
T3 - Proceedings - 2014 IEEE International Conference on Granular Computing, GrC 2014
SP - 166
EP - 169
BT - Proceedings - 2014 IEEE International Conference on Granular Computing, GrC 2014
A2 - Kudo, Yasuo
A2 - Tsumoto, Shusaku
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 -