Research of SVM ensembles in medical examination scheduling

Yi Du (), Hua Yu () and Zhijun Li ()
Additional contact information
Yi Du: Shanghai Polytechnic University
Hua Yu: Shanghai General Hospital
Zhijun Li: Shanghai Dayuan Culture Media Co, Ltd

Journal of Combinatorial Optimization, No 0, 11 pages

Abstract: Abstract In order to solve the problem of deterioration of the generalization ability caused by support vector machine (SVM), this paper proposes a regression prediction method based on SVM ensemble learning. The grid search method is used to optimize the modeling parameters of an SVM-based predictor. An AdaBoost method is used to integrate multiple SVM-based predictors, and a regression prediction model based on SVM ensemble learning is constructed. Using the database collected by a hospital taken as the research object, the executing time prediction of outpatient examination scheduling was tested and compared with the experimental results of the SVM predictor. The results show that the ensemble learning algorithm can effectively reduce the computational complexity brought in by training all samples altogether and improve the prediction accuracy. The prediction instability and low precision of the sampling-based standard SVM predictor are also solved effectively.

Keywords: SVM ensemble; Regression prediction; AdaBoost; Outpatient medical examination scheduling (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10878-019-00510-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:jcomop:v::y::i::d:10.1007_s10878-019-00510-1

Ordering information: This journal article can be ordered from
https://www.springer.com/journal/10878

DOI: 10.1007/s10878-019-00510-1

Access Statistics for this article

Journal of Combinatorial Optimization is currently edited by Thai, My T.

More articles in Journal of Combinatorial Optimization from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().