AccScience Publishing / AIH / Online First / DOI: 10.36922/AIH025470107
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ORIGINAL RESEARCH ARTICLE

Temporal correlation rough set-based three-way clustering model for chronic kidney disease diagnosis

Yan Zhang1* ,  Wanzhen Long2 ,  Ruihui Chen3 ,  Zihui Lin3 ,  Haole Huang3 ,  Lincong Li3 ,  Xiaoli Chu4,5*
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1 Intelligent Computing and Big Data Technology Research Center, Economics and Management Experimental Teaching Center, Guangdong University of Finance and Economics, Guangzhou, Guangdong, China
2 Department of Computer Application, School of Big Data and Artificial Intelligence, Guangdong University of Finance and Economics, Guangzhou, Guangdong, China
3 Department of Data Science, School of Statistics and Data Science, Guangdong University of Finance and Economics, Guangzhou, Guangdong, China
4 State Key Laboratory of Traditional Chinese Medicine Syndrome, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
5 Department of Traditional Chinese Medicine Big Data Research, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
AIH, 025470107 https://doi.org/10.36922/AIH025470107
Received: 23 November 2025 | Revised: 19 January 2026 | Accepted: 28 January 2026 | Published online: 3 April 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Chronic non-communicable diseases (CNCDs) are the leading cause of morbidity and mortality worldwide. Currently, diagnostic information for CNCDs remains incomplete and is subject to ongoing change, leading to uncertainty in diagnostic conclusions and severely impacting disease control. Therefore, it is necessary to study decision-making theory and methods for assisting clinical diagnosis. Given the characteristics of clinical diagnostic decision problems, this paper discusses the temporally correlated rough set theory and its three-way clustering model. Firstly, the temporal correlation rough set model based on the gradient and cosine similarity is defined. Then, the clinical diagnostic decision process for CNCDs is transformed into a three-way clustering problem with temporal correlation attributes. A three-way clustering model based on a temporal correlation rough set is constructed to support clinical diagnosis decisions for CNCDs. In this model, the common features of temporal association attributes are identified using a positive domain-based three-way clustering algorithm. Finally, the validity and applicability of the theoretical model are verified using real clinical data from 80,139 visit time points and 83 laboratory examination indicators from 3,094 chronic kidney disease (CKD) patients. Based on the algorithm’s calculations and a retrospective literature review, β2-microglobulin is identified as a candidate marker for CKD that warrants further validation and could provide auxiliary decision support for clinical practice. The main contribution of this paper is twofold. One is to make a new theoretical contribution to the data-driven research paradigm of clinical diagnosis decision-making. Another is to provide quantitative decision support for clinical diagnosis.

Graphical abstract
Keywords
Rough sets
Three-way clustering
Clinical decision-making
Temporal correlation features
Funding
The work was supported by the National Natural Science Foundation of China (72301082), the China Post doctoral Science Foundation (2025M773863), the Guangdong Basic and Applied Basic Research Foundation (2022A1515110703), the Guangdong Provincial Hospital of Chinese Medicine Science and Technology Research Project (YN2022QN33, YN2024GZRPY077), the Guangzhou Key Research and Development Program (202206010101), the National Key Laboratory of Chinese Medicine Syndrome (QZ2023ZZ07), the Postdoctoral Fellowship Program of CPSF under Grant (GZC20252561), the Special Project of State Key Laboratory of Dampness Syndrome of Chinese Medicine (SZ2021ZZ36, SZ2021ZZ09), the Guangzhou Science and Technology Plan Project (2024A03J0117, 2025A03J4062) and the 2020 Guangdong Provincial Science and Technology Innovation Strategy Special Fund (Guangdong-Hong Kong-Macau Joint Lab) (2020B1212030006).
Conflict of interest
The authors declare they have no competing interests.
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Artificial Intelligence in Health, Electronic ISSN: 3029-2387 Print ISSN: 3041-0894, Published by AccScience Publishing
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