IDEAS home Printed from https://ideas.repec.org/a/spr/jcomop/vyid10.1007_s10878-019-00486-y.html
   My bibliography  Save this article

Research on model and algorithm of TCM constitution identification based on artificial intelligence

Author

Listed:
  • Bin Li

    (Shanghai Polytechnic University)

  • Qianghua Wei

    (Shanghai Jiaotong University)

  • Xinye Zhou

    (Shanghai Polytechnic University)

Abstract

In recent years, the research and application of artificial intelligence are developing rapidly. The application of artificial intelligence in medical image judgment has achieved good results in accuracy and speed. As big data and computing power increase, artificial intelligence will find more applications in medicine and health. In this paper, the artificial intelligence technology is applied to the judgment of Traditional Chinese Medicine (TCM) constitutional type. Using the model and algorithm of neural network, the fuzzy linguistic variables are expressed in value of membership degree to construct the nine standard TCM constitutional types as the basic sample data. Then it is combined with the judgment results of several TCM doctors to form new sample data and the model is trained by algorithm. The trained model is used to help TCM to classify individuals’ constitution. The simulation results show that the model achieves a good result by learning the sample data.

Suggested Citation

  • Bin Li & Qianghua Wei & Xinye Zhou, 0. "Research on model and algorithm of TCM constitution identification based on artificial intelligence," Journal of Combinatorial Optimization, Springer, vol. 0, pages 1-16.
  • Handle: RePEc:spr:jcomop:v::y::i::d:10.1007_s10878-019-00486-y
    DOI: 10.1007/s10878-019-00486-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10878-019-00486-y
    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/s10878-019-00486-y?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. Xiaohui Liu & Ni Zou & Dan Zhu & Dan Wang, 2019. "Influencing factors analysis and modeling of hospital-acquired infection in elderly patients," Journal of Combinatorial Optimization, Springer, vol. 37(1), pages 248-270, January.
    2. Feng Zhang & Jing Li & Junxiang Fan & Huili Shen & Jian Shen & Hua Yu, 2019. "Three-dimensional stable matching with hybrid preferences," Journal of Combinatorial Optimization, Springer, vol. 37(1), pages 330-336, January.
    3. 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.
    Full references (including those not matched with items on IDEAS)

    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. Bin Li & Qianghua Wei & Xinye Zhou, 2021. "Research on model and algorithm of TCM constitution identification based on artificial intelligence," Journal of Combinatorial Optimization, Springer, vol. 42(4), pages 988-1003, November.
    2. Jing Fan & Hui Shi, 0. "A three-stage supply chain scheduling problem based on the nursing assistants’ daily work in a hospital," Journal of Combinatorial Optimization, Springer, vol. 0, pages 1-13.
    3. Gang Du & Xi Liang & Xiaoling Ouyang & Chunming Wang, 0. "Risk prediction of hypertension complications based on the intelligent algorithm optimized Bayesian network," Journal of Combinatorial Optimization, Springer, vol. 0, pages 1-22.
    4. He Huang & Wei Gao & Chunming Ye, 2021. "An intelligent data-driven model for disease diagnosis based on machine learning theory," Journal of Combinatorial Optimization, Springer, vol. 42(4), pages 884-895, November.
    5. Ruiping Wang & Mei Wang & Jian Chang & Zai Luo & Feng Zhang & Chen Huang, 2021. "An optimized approach of venous thrombus embolism risk assessment," Journal of Combinatorial Optimization, Springer, vol. 42(4), pages 1053-1063, November.
    6. Ruiping Wang & Mei Wang & Jian Chang & Zai Luo & Feng Zhang & Chen Huang, 0. "An optimized approach of venous thrombus embolism risk assessment," Journal of Combinatorial Optimization, Springer, vol. 0, pages 1-11.
    7. Xin Yan & Hongmiao Zhu & Jian Luo, 0. "A novel kernel-free nonlinear SVM for semi-supervised classification in disease diagnosis," Journal of Combinatorial Optimization, Springer, vol. 0, pages 1-18.
    8. Feng Zhang & Liwei Zhong, 0. "Three-sided matching problem with mixed preferences," Journal of Combinatorial Optimization, Springer, vol. 0, pages 1-9.
    9. He Huang & Wei Gao & Chunming Ye, 0. "An intelligent data-driven model for disease diagnosis based on machine learning theory," Journal of Combinatorial Optimization, Springer, vol. 0, pages 1-12.
    10. Xin Yan & Hongmiao Zhu & Jian Luo, 2021. "A novel kernel-free nonlinear SVM for semi-supervised classification in disease diagnosis," Journal of Combinatorial Optimization, Springer, vol. 42(4), pages 948-965, November.
    11. Gang Du & Xi Liang & Xiaoling Ouyang & Chunming Wang, 2021. "Risk prediction of hypertension complications based on the intelligent algorithm optimized Bayesian network," Journal of Combinatorial Optimization, Springer, vol. 42(4), pages 966-987, November.
    12. He Huang & Po-Chou Shih & Yuelan Zhu & Wei Gao, 2022. "An integrated model for medical expense system optimization during diagnosis process based on artificial intelligence algorithm," Journal of Combinatorial Optimization, Springer, vol. 44(4), pages 2515-2532, November.
    13. Shi Yin & Jian Chang & Hailan Pan & Haizhou Mao & Mei Wang, 0. "Early warning of venous thromboembolism after surgery based on self-organizing competitive network," Journal of Combinatorial Optimization, Springer, vol. 0, pages 1-19.
    14. Feng Zhang & Liwei Zhong, 2021. "Three-sided matching problem with mixed preferences," Journal of Combinatorial Optimization, Springer, vol. 42(4), pages 928-936, November.
    15. Jing Fan & Hui Shi, 2021. "A three-stage supply chain scheduling problem based on the nursing assistants’ daily work in a hospital," Journal of Combinatorial Optimization, Springer, vol. 42(4), pages 896-908, November.
    16. Jorge Arenas & Juan Pablo Torres-Martínez, 2023. "Reconsidering the existence of stable solutions in three-sided matching problems with mixed preferences," Journal of Combinatorial Optimization, Springer, vol. 45(2), pages 1-8, March.
    17. Shi Yin & Jian Chang & Hailan Pan & Haizhou Mao & Mei Wang, 2021. "Early warning of venous thromboembolism after surgery based on self-organizing competitive network," Journal of Combinatorial Optimization, Springer, vol. 42(4), pages 909-927, November.
    18. Jorge Arenas & Juan Pablo Torres-Martinez, 2024. "On Incentives in Three-Sided Markets," Working Papers wp558, University of Chile, Department of Economics.

    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::y::i::d:10.1007_s10878-019-00486-y. 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.