International Journal of Computational Intelligence Systems

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Volume 11, Issue 1, 2018, Pages 873 - 881

An Agile Mortality Prediction Model: Hybrid Logarithm Least-Squares Support Vector Regression with Cautious Random Particle Swarm Optimization

Authors
Chien-Lung Chan1, clchan@saturn.yzu.edu.tw, Chia-Li Chen2, *, carey71@mail.lhu.edu.tw, Hsien-Wei Ting3, ting.ns@gmail.com, Dinh-Van Phan4, dvan2707@due.edu.vn
1Department of Information Management, Yuan Ze University, Innovation Center for Big Data and Digital Convergence, No.135, Fareastern Rd., Chungli Dist., Taoyuan City 320, Taiwan (R.O.C.)
2Department of Information Management, Lung Hwa University, Department of Information Management, Yuan Ze University, No.300, Sec.1, Wanshou Rd., Guishan Dist, Taoyuan County 33306, Taiwan (R.O.C.)
3Department of Neurosurgery, Taipei Hospital, Department of Health, No.127, Suyuan Rd, Hsinchuang Dist, NewTaipei City 242-13 , Taiwan (R.O.C.)
4Department of Information Management, Yuan Ze University, No.135, Fareastern Rd., Chungli Dist., Taoyuan City 320, Taiwan (R.O.C.)
*Corresponding author.
Corresponding Author
Received 30 October 2016, Accepted 16 August 2017, Available Online 1 January 2018.
DOI
10.2991/ijcis.11.1.66How to use a DOI?
Keywords
intensive care; mortality prediction; logarithm least-squares support vector regression; cautious random particle swarm optimization
Abstract

Logarithm Least-Squares Support Vector Regression (LLS-SVR) has been applied in addressing forecasting problems in various fields, including bioinformatics, financial time series, electronics, plastic injection moulding, Chemistry and cost estimations. Cautious Random Particle Swarm Optimization (CRPSO) uses random values that allow pbest and gbest to be adjusted to the correct weight using a random value. CRPSO limits the random value to be conditional, to avoid premature convergence into a local optimum. If the random value is greater than 0.6, another random value is chosen. The movement of the range (cautious flow) is controlled to avoid premature convergence. This pilot study retrospectively collected data on 695 patients admitted to intensive care units and constructed a novel mortality prediction model with logarithm least-squares support vector regression (LLS-SVR) and cautious random particle swarm optimization (CRPSO). LLS-SVR-CRPSO was employed to optimally select the parameters of the hybrid system. This new mortality model can offer agile support for physicians’ intensive care decision-making.

Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
11 - 1
Pages
873 - 881
Publication Date
2018/01/01
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.2991/ijcis.11.1.66How to use a DOI?
Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

Cite this article

risenwbib
TY  - JOUR
AU  - Chien-Lung Chan
AU  - Chia-Li Chen
AU  - Hsien-Wei Ting
AU  - Dinh-Van Phan
PY  - 2018
DA  - 2018/01/01
TI  - An Agile Mortality Prediction Model: Hybrid Logarithm Least-Squares Support Vector Regression with Cautious Random Particle Swarm Optimization
JO  - International Journal of Computational Intelligence Systems
SP  - 873
EP  - 881
VL  - 11
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.11.1.66
DO  - 10.2991/ijcis.11.1.66
ID  - Chan2018
ER  -