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Kernel alignment-based three-way clustering on attribute space and its application in stroke risk identification

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Abstract

Identifying the key risk factors of disease from a large amount of clinical data is a prerequisite for further scientific decision-making. In medical practice, the clinical symptom information of patients usually includes various types of data. Meanwhile, the occurrence and development of diseases are joint result of the mutual influence factors. Therefore, there is usually a correlation between attributes. In this paper, we discuss a kind of hybrid attribute feature selection problem considering the correlation between attributes. Firstly, we take the identification of disease pathogenic factors in medical decision as the background, and construct a hybrid attribute decision system. Secondly, by introducing kernel alignment, the uncertain relationship between attributes is defined. Based on this, a three-way clustering model in attribute space is established. Furthermore, a feature selection method for hybrid attribute data based on three-way clustering in attribute space is proposed. Finally, we applied the proposed model to identify the pathogenic factors of stroke and used 279 clinical random samples for simulation analysis. The results verified the applicability and validity of the model. The main contributions of this paper include two aspects. In terms of theory, by introducing kernel alignment, a three-way clustering algorithm in attribute space is established. Meanwhile, a hybrid attribute feature selection method based on three-way clustering is proposed. In terms of application, the proposed method is applied to identify risk factors of stroke.

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Acknowledgements

The work was partly supported by the National Natural Science Foundation of China (72071152, 71571090, and 61871141), the Xi’an Science and Technology Projects (XA2020-RKXYJ-0086), the Youth Innovation Team of Shaanxi Universities, the China Postdoctoral Science Foundation (2020M670046ZX), the Science and Technology Plan Project of Yulin (19-50), the Project of Shaanxi Key Laboratory of BrainDisorders (No.20NBZD02), Special Project of State Key Laboratory of Dampness Syndrome of Chinese Medicine (No.SZ2020ZZ02), the Guangzhou Key Research and Development Program (2022), the Philosophy and Social Science Planning Project of Gansu Province (No. 2021YB059).

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Authors and Affiliations

  1. School of Economics and Management, Xidian University, Xi’an, Shaanxi, People’s Republic of China

    Ting Wang, Bingzhen Sun & Xiaoli Chu

  2. The Third Department of Neurology, The Second Affiliated Hospital of Xi’an Medical University&Shaanxi Key Laboratory of Brain Disorders, Xi’an, Shaanxi, People’s Republic of China

    Chao Jiang

  3. State Key Laboratory of Dampness Syndrome of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, People’s Republic of China

    Heng Weng & Xiaoli Chu

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  1. Ting Wang
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  2. Bingzhen Sun
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  3. Chao Jiang
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  5. Xiaoli Chu
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Correspondence to Bingzhen Sun, Chao Jiang or Heng Weng.

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Wang, T., Sun, B., Jiang, C. et al. Kernel alignment-based three-way clustering on attribute space and its application in stroke risk identification. Int. J. Mach. Learn. & Cyber. 13, 1697–1711 (2022). https://doi.org/10.1007/s13042-021-01478-3

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