IDEAS home Printed from https://ideas.repec.org/a/spr/jcomop/v42y2021i4d10.1007_s10878-019-00510-1.html
   My bibliography  Save this article

Research of SVM ensembles in medical examination scheduling

Author

Listed:
  • Yi Du

    (Shanghai Polytechnic University)

  • Hua Yu

    (Shanghai General Hospital)

  • Zhijun Li

    (Shanghai Dayuan Culture Media Co, Ltd)

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.

Suggested Citation

  • Yi Du & Hua Yu & Zhijun Li, 2021. "Research of SVM ensembles in medical examination scheduling," Journal of Combinatorial Optimization, Springer, vol. 42(4), pages 1042-1052, November.
    • Handle: RePEc:spr:jcomop:v:42:y:2021:i:4:d:10.1007_s10878-019-00510-1
    DOI: 10.1007/s10878-019-00510-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10878-019-00510-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10878-019-00510-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Jonathan Patrick & Martin L. Puterman & Maurice Queyranne, 2008. "Dynamic Multipriority Patient Scheduling for a Diagnostic Resource," Operations Research, INFORMS, vol. 56(6), pages 1507-1525, December.
    2. Shan Wang & Huiqiao Su & Guohua Wan, 2015. "Resource-constrained machine scheduling with machine eligibility restriction and its applications to surgical operations scheduling," Journal of Combinatorial Optimization, Springer, vol. 30(4), pages 982-995, November.
    3. Yanqin Bai & Xiao Han & Tong Chen & Hua Yu, 2015. "Quadratic kernel-free least squares support vector machine for target diseases classification," Journal of Combinatorial Optimization, Springer, vol. 30(4), pages 850-870, November.
    4. West, David & Mangiameli, Paul & Rampal, Rohit & West, Vivian, 2005. "Ensemble strategies for a medical diagnostic decision support system: A breast cancer diagnosis application," European Journal of Operational Research, Elsevier, vol. 162(2), pages 532-551, April.
    5. Linda V. Green & Sergei Savin & Ben Wang, 2006. "Managing Patient Service in a Diagnostic Medical Facility," Operations Research, INFORMS, vol. 54(1), pages 11-25, February.
    6. Diwakar Gupta & Lei Wang, 2008. "Revenue Management for a Primary-Care Clinic in the Presence of Patient Choice," Operations Research, INFORMS, vol. 56(3), pages 576-592, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. He Huang & Liwei Zhong & Ting Shen & Huixin Wang, 2022. "Performance prediction and optimization for healthcare enterprises in the context of the COVID-19 pandemic: an intelligent DEA-SVM model," Journal of Combinatorial Optimization, Springer, vol. 44(5), pages 3778-3791, December.
    2. Aiyar, Ria & Due, Clemence & Taylor, Amanda M. & Sawyer, Alyssa C.P., 2023. "The wellbeing and support experiences of parents and caregivers from South and Southeast Asian refugee backgrounds during the First 2000 Days: A systematic review," Children and Youth Services Review, Elsevier, vol. 155(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yi Du & Hua Yu & Zhijun Li, 0. "Research of SVM ensembles in medical examination scheduling," Journal of Combinatorial Optimization, Springer, vol. 0, pages 1-11.
    2. Miao Bai & Bjorn Berg & Esra Sisikoglu Sir & Mustafa Y. Sir, 2023. "Partially partitioned templating strategies for outpatient specialty practices," Production and Operations Management, Production and Operations Management Society, vol. 32(1), pages 301-318, January.
    3. Gang Du & Xinyue Li & Hui Hu & Xiaoling Ouyang, 2018. "Optimizing Daily Service Scheduling for Medical Diagnostic Equipment Considering Patient Satisfaction and Hospital Revenue," Sustainability, MDPI, vol. 10(9), pages 1-23, September.
    4. Yongbo Xiao & Yan Zhu, 2016. "Value management of diagnostic equipment with cancelation, no‐show, and emergency patients," Naval Research Logistics (NRL), John Wiley & Sons, vol. 63(4), pages 287-304, June.
    5. Seokjun Youn & H. Neil Geismar & Michael Pinedo, 2022. "Planning and scheduling in healthcare for better care coordination: Current understanding, trending topics, and future opportunities," Production and Operations Management, Production and Operations Management Society, vol. 31(12), pages 4407-4423, December.
    6. Namakshenas, Mohammad & Mazdeh, Mohammad Mahdavi & Braaksma, Aleida & Heydari, Mehdi, 2023. "Appointment scheduling for medical diagnostic centers considering time-sensitive pharmaceuticals: A dynamic robust optimization approach," European Journal of Operational Research, Elsevier, vol. 305(3), pages 1018-1031.
    7. Paola Cappanera & Filippo Visintin & Carlo Banditori & Daniele Feo, 2019. "Evaluating the long-term effects of appointment scheduling policies in a magnetic resonance imaging setting," Flexible Services and Manufacturing Journal, Springer, vol. 31(1), pages 212-254, March.
    8. Hans-Jörg Schütz & Rainer Kolisch, 2013. "Capacity allocation for demand of different customer-product-combinations with cancellations, no-shows, and overbooking when there is a sequential delivery of service," Annals of Operations Research, Springer, vol. 206(1), pages 401-423, July.
    9. Ahmadi-Javid, Amir & Jalali, Zahra & Klassen, Kenneth J, 2017. "Outpatient appointment systems in healthcare: A review of optimization studies," European Journal of Operational Research, Elsevier, vol. 258(1), pages 3-34.
    10. Mehmet A. Begen & Maurice Queyranne, 2011. "Appointment Scheduling with Discrete Random Durations," Mathematics of Operations Research, INFORMS, vol. 36(2), pages 240-257, May.
    11. Van-Anh Truong, 2015. "Optimal Advance Scheduling," Management Science, INFORMS, vol. 61(7), pages 1584-1597, July.
    12. Jingui Xie & Weifen Zhuang & Marcus Ang & Mabel C. Chou & Li Luo & David D. Yao, 2021. "Analytics for Hospital Resource Planning—Two Case Studies," Production and Operations Management, Production and Operations Management Society, vol. 30(6), pages 1863-1885, June.
    13. Lu, Yuwei & Xie, Xiaolan & Jiang, Zhibin, 2018. "Dynamic appointment scheduling with wait-dependent abandonment," European Journal of Operational Research, Elsevier, vol. 265(3), pages 975-984.
    14. Tugba Cayirli & Pinar Dursun & Evrim D. Gunes, 2019. "An integrated analysis of capacity allocation and patient scheduling in presence of seasonal walk-ins," Flexible Services and Manufacturing Journal, Springer, vol. 31(2), pages 524-561, June.
    15. Matthias Deceuninck & Stijn Vuyst & Dieter Claeys & Dieter Fiems, 2021. "Appointment games with unobservable and observable schedules," Annals of Operations Research, Springer, vol. 307(1), pages 93-110, December.
    16. Na Geng & Letian Chen & Ran Liu & Yanhong Zhu, 2017. "Optimal patient assignment for W queueing network in a diagnostic facility setting," International Journal of Production Research, Taylor & Francis Journals, vol. 55(19), pages 5609-5631, October.
    17. Wei Gao & Wuping Bao & Xin Zhou, 2019. "Analysis of cough detection index based on decision tree and support vector machine," Journal of Combinatorial Optimization, Springer, vol. 37(1), pages 375-384, January.
    18. Katsumi Morikawa & Katsuhiko Takahashi & Daisuke Hirotani, 2018. "Performance evaluation of candidate appointment schedules using clearing functions," Journal of Intelligent Manufacturing, Springer, vol. 29(3), pages 509-518, March.
    19. Isabel Kaluza & Guido Voigt & Knut Haase & Antonia Dietze, 2024. "Control of Online-Appointment Systems When the Booking Status Signals Quality of Service," Schmalenbach Journal of Business Research, Springer, vol. 76(3), pages 397-432, September.
    20. Esmaeil Keyvanshokooh & Pooyan Kazemian & Mohammad Fattahi & Mark P. Van Oyen, 2022. "Coordinated and Priority‐Based Surgical Care: An Integrated Distributionally Robust Stochastic Optimization Approach," Production and Operations Management, Production and Operations Management Society, vol. 31(4), pages 1510-1535, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:jcomop:v:42:y:2021:i:4:d:10.1007_s10878-019-00510-1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.