Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2020 Nov 26:2020:9081641.
doi: 10.1155/2020/9081641. eCollection 2020.

A Microcosmic Syndrome Differentiation Model for Metabolic Syndrome with Multilabel Learning

Shujie Xia  1 , Jia Zhang  2 , Guodong Du  2 , Shaozi Li  2 , Chi Teng Vong  3 , Zhaoyang Yang  1 , Jiliang Xin  1 , Long Zhu  1 , Bizhen Gao  1 , Candong Li  1
Affiliations

A Microcosmic Syndrome Differentiation Model for Metabolic Syndrome with Multilabel Learning

Shujie Xia et al. Evid Based Complement Alternat Med. .

Abstract

Background: Metabolic syndrome (MS) is a complex multisystem disease. Traditional Chinese medicine (TCM) is effective in preventing and treating MS. Syndrome differentiation is the basis of TCM treatment, which is composed of location and/or nature syndrome elements. At present, there are still some problems for objective and comprehensive syndrome differentiation in MS. This study mainly proposes a solution to two problems. Firstly, TCM syndromes are concurrent, that is, multiple TCM syndromes may develop in the same patient. Secondly, there is a lack of holistic exploration of the relationship between microscopic indexes, and TCM syndromes. In regard to these two problems, multilabel learning (MLL) method in machine learning can be used to solve them, and a microcosmic syndrome differentiation model can also be built innovatively, which can provide a foundation for the establishment of the next model of multidimensional syndrome differentiation in MS.

Methods: The standardization scale of TCM four diagnostic information for MS was designed, which was used to obtain the results of TCM diagnosis. The model of microcosmic syndrome differentiation was constructed based on 39 physicochemical indexes by MLL techniques, called ML-kNN. Firstly, the multilabel learning method was compared with three commonly used single learning algorithms. Then, the results from ML-kNN were compared between physicochemical indexes and TCM information. Finally, the influence of the parameter k on the diagnostic model was investigated and the best k value was chosen for TCM diagnosis.

Results: A total of 698 cases were collected for the modeling of the microcosmic diagnosis of MS. The comprehensive performance of the ML-kNN model worked obviously better than the others, where the average precision of diagnosis was 71.4%. The results from ML-kNN based on physicochemical indexes were similar to the results based on TCM information. On the other hand, the k value had less influence on the prediction results from ML-kNN.

Conclusions: In the present study, the microcosmic syndrome differentiation model of MS with MLL techniques was good at predicting syndrome elements and could be used to solve the diagnosis problems of multiple labels. Besides, it was suggested that there was a complex correlation between TCM syndrome elements and physicochemical indexes, which worth future investigation to promote the development of objective differentiation of MS.

PubMed Disclaimer

Conflict of interest statement

All authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
The process of TCM syndrome differentiation.
Figure 2
Figure 2
A paradigm of the proposed method.
Figure 3
Figure 3
The score distribution of the syndrome elements.
Figure 4
Figure 4
The fluctuation on the average precision of four machine learning in the process of cross-validation.
Figure 5
Figure 5
The comparison of the prediction performances of ML-kNN using physicochemical indexes and TCM information.
Figure 6
Figure 6
The influence of ML-kNN algorithm using physicochemical indexes on the prediction results with different k values.
Figure 7
Figure 7
The influence of different syndrome elements on the average precision of ML-kNN using physicochemical indexes.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Wang J., Liang D., Qu Z., Kislyakov I., Kiselev V., Liu J. PEGylated-folic acid–modified black phosphorus quantum dots as near-infrared agents for dual-modality imaging-guided selective cancer cell destruction. Nanophotonics. 2020;9(8):2425–2435. doi: 10.1515/nanoph-2019-0506. - DOI
    1. Zhang Y., Li Y., Ni Q. Diagnosis and treatment of metabolic syndrome. Chinese Clinical Doctor. 2018;46(11):p. 4.
    1. Zhou J., Wang Q., Xiang Z., et al. Network pharmacology analysis of traditional Chinese medicine formula xiao ke Yin shui treating type 2 diabetes mellitus. Evidence Based Complementary and Alternative Medicine. 2019;2019 doi: 10.1155/2019/4202563.4202563 - DOI - PMC - PubMed
    1. Tan A., Huang H., Zhang P., Li S. Network-based cancer precision medicine: a new emerging paradigm. Cancer Letters. 2019;458:39–45. doi: 10.1016/j.canlet.2019.05.015. - DOI - PubMed
    1. Wang Q., Liu L. Advances in the treatment of metabolic syndrome by traditional Chinese medicine. Information on Traditional Chinese Medicine. 2017;14(7):p. 5.

LinkOut - more resources