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Double-quantitative multi-granularity kernel fuzzy rough sets model and its application in rheumatoid arthritis risk assessment

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Abstract

The medical big data of combined Chinese and western medicine diagnosis and treatment (CCWMDT) is difficult to be used effectively in clinical medical decision-making because of its complex structure and multi-source storage. In this paper, we discuss the problem of multi-attribute group decision making (MAGDM) in complex information systems with multi-source, diversified and hybrid attribute information. With the help of traditional information systems, a hybrid diversified attribute information system (HDAIS) is first defined. Further, with the involvement of kernel functions, a binary relation over HDAISs is then presented. At the same time, in response to the problem of inaccurate information, a double-quantitative model is introduced to extract effective information from the perspective of absolute and relative metrics, and the rough approximations of a decision target concept under the multi-granularity framework are given, namely, a double-quantitative multi-granularity kernel fuzzy rough set (DQ-MGKFRS). Based on the above, a group decision method is established on DQ-MGKFRS to realize the information fusion of HDAIS. Finally, the proposed method used in the medical decision problem of rheumatoid arthritis (RA) diagnosis. The results of comparative analysis and sensitivity analysis illustrate the feasibility, effectiveness and robustness of our method.

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Acknowledgements

The work was supported by the National Natural Science Foundation of China (No. 72071152,72301082), the Shaanxi National Funds for Distinguished Young Scientists, China (No. 2023-JC-JQ-11), the Fundamental Research Funds for the Central Universities (No. ZYTS24049), the Guangzhou Key Research and Development Program (No. 202206010101), the Guangdong Basic and Applied Basic Research Foundation (No. 2022A1515110703), the Guangdong Provincial Hospital of Chinese Medicine Science and Technology Research Project (No. YN2022QN33).

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Authors and Affiliations

  1. School of Economics and Management, Xidian University, Xi’an, 710126, China

    Xianjun Dai, Bingzhen Sun, Juncheng Bai & Jin Ye

  2. State Key Laboratory of Dampness Syndrome of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, Guangdong, China

    Xiaoli Chu

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Contributions

Xianjun Dai: Conceptualization, Methodology, Investigation, Writing-original draft. Bingzhen Sun: Conceptualization, Methodology, Investigation, Writing-original draft, Supervision, Funding acquisition. Juncheng Bai: Investigation. Jin Ye: Investigation. Xiaoli Chu: Data curation, Funding acquisition. All authors reviewed the manuscript.

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Correspondence to Juncheng Bai or Xiaoli Chu.

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Table 9 shows the full names of all abbreviations that appear in this article.

Table 9 Abbreviations used throughout the article
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Dai, X., Sun, B., Bai, J. et al. Double-quantitative multi-granularity kernel fuzzy rough sets model and its application in rheumatoid arthritis risk assessment. Int. J. Mach. Learn. & Cyber. (2024). https://doi.org/10.1007/s13042-024-02144-0

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