A Chronic Kidney Disease Diagnostic Model Based on an Interpretable Deep Belief Rule Base
Chronic Kidney Disease (CKD) has become a serious public health problem because of its characteristic ’three highs and one low’: high prevalence, high disability rate, high medical costs, and low awareness. Therefore, an accurate diagnosis of CKD is crucial. Given the unique na...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10883992/ |
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| author | Yawen Zhao Kexin Zhang Xiping Duan Shuping Che Ning Ma |
| author_facet | Yawen Zhao Kexin Zhang Xiping Duan Shuping Che Ning Ma |
| author_sort | Yawen Zhao |
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| description | Chronic Kidney Disease (CKD) has become a serious public health problem because of its characteristic ’three highs and one low’: high prevalence, high disability rate, high medical costs, and low awareness. Therefore, an accurate diagnosis of CKD is crucial. Given the unique nature of the medical field, it is essential to ensure that the results of the CKD diagnostic model are trustworthy to doctors and patients and are practically applicable. Belief Rule Base (BRB) models explain their results through transparent reasoning processes and belief distributions. However, existing BRB models are typically constructed as one-time setups that require reconstruction to include new patient indicators. Furthermore, redundant rules within BRB models increase the difficulty of optimization, and randomness in the optimization process can affect the interpretability of the model. To address these challenges, this paper presents a CKD diagnostic model based on an interpretable Deep Belief Rule Base (DBRB-I). The model leverages a deep BRB structure to enhance scalability and prevent rule explosions. Additionally, it introduces a novel rule reduction method that incorporates expert knowledge to simplify redundant rules in an interpretable way, thus easing the optimization process. To preserve the model’s interpretability, the optimization algorithm includes constraints grounded in expert knowledge. Case studies and comparisons with seven other methods show that the CKD diagnostic model based on DBRB-I outperforms the others in a practical diagnostic setting. |
| format | Article |
| id | doaj-art-246b6b1e42004ca48b204484872cc6bf |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| series | IEEE Access |
| spelling | doaj-art-246b6b1e42004ca48b204484872cc6bf2025-08-20T03:11:54ZengIEEEIEEE Access2169-35362025-01-0113297182973710.1109/ACCESS.2025.354139110883992A Chronic Kidney Disease Diagnostic Model Based on an Interpretable Deep Belief Rule BaseYawen Zhao0Kexin Zhang1https://orcid.org/0009-0009-0767-7309Xiping Duan2https://orcid.org/0000-0001-5031-3601Shuping Che3https://orcid.org/0009-0004-6505-5640Ning Ma4School of Computer Science and Information Engineering, Harbin Normal University, Harbin, ChinaSchool of Computer Science and Information Engineering, Harbin Normal University, Harbin, ChinaSchool of Computer Science and Information Engineering, Harbin Normal University, Harbin, ChinaCT Department, The First Affiliated Hospital of Harbin Medical University, Harbin, ChinaSchool of Computer Science and Information Engineering, Harbin Normal University, Harbin, ChinaChronic Kidney Disease (CKD) has become a serious public health problem because of its characteristic ’three highs and one low’: high prevalence, high disability rate, high medical costs, and low awareness. Therefore, an accurate diagnosis of CKD is crucial. Given the unique nature of the medical field, it is essential to ensure that the results of the CKD diagnostic model are trustworthy to doctors and patients and are practically applicable. Belief Rule Base (BRB) models explain their results through transparent reasoning processes and belief distributions. However, existing BRB models are typically constructed as one-time setups that require reconstruction to include new patient indicators. Furthermore, redundant rules within BRB models increase the difficulty of optimization, and randomness in the optimization process can affect the interpretability of the model. To address these challenges, this paper presents a CKD diagnostic model based on an interpretable Deep Belief Rule Base (DBRB-I). The model leverages a deep BRB structure to enhance scalability and prevent rule explosions. Additionally, it introduces a novel rule reduction method that incorporates expert knowledge to simplify redundant rules in an interpretable way, thus easing the optimization process. To preserve the model’s interpretability, the optimization algorithm includes constraints grounded in expert knowledge. Case studies and comparisons with seven other methods show that the CKD diagnostic model based on DBRB-I outperforms the others in a practical diagnostic setting.https://ieeexplore.ieee.org/document/10883992/Belief rule basedisease diagnosisrule reductionevidence reasoning |
| spellingShingle | Yawen Zhao Kexin Zhang Xiping Duan Shuping Che Ning Ma A Chronic Kidney Disease Diagnostic Model Based on an Interpretable Deep Belief Rule Base IEEE Access Belief rule base disease diagnosis rule reduction evidence reasoning |
| title | A Chronic Kidney Disease Diagnostic Model Based on an Interpretable Deep Belief Rule Base |
| title_full | A Chronic Kidney Disease Diagnostic Model Based on an Interpretable Deep Belief Rule Base |
| title_fullStr | A Chronic Kidney Disease Diagnostic Model Based on an Interpretable Deep Belief Rule Base |
| title_full_unstemmed | A Chronic Kidney Disease Diagnostic Model Based on an Interpretable Deep Belief Rule Base |
| title_short | A Chronic Kidney Disease Diagnostic Model Based on an Interpretable Deep Belief Rule Base |
| title_sort | chronic kidney disease diagnostic model based on an interpretable deep belief rule base |
| topic | Belief rule base disease diagnosis rule reduction evidence reasoning |
| url | https://ieeexplore.ieee.org/document/10883992/ |
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