A dominant set-informed interpretable fuzzy system for automated diagnosis of dementia
Dementia is an incurable neurodegenerative disease primarily affecting the older population, for which the World Health Organisation has set to promoting early diagnosis and timely management as one of the primary goals for dementia care. While a range of popular machine learning algorithms and thei...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-08-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2022.867664/full |
| _version_ | 1818492325046779904 |
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| author | Tianhua Chen Pan Su Yinghua Shen Lu Chen Mufti Mahmud Yitian Zhao Grigoris Antoniou |
| author_facet | Tianhua Chen Pan Su Yinghua Shen Lu Chen Mufti Mahmud Yitian Zhao Grigoris Antoniou |
| author_sort | Tianhua Chen |
| collection | DOAJ |
| description | Dementia is an incurable neurodegenerative disease primarily affecting the older population, for which the World Health Organisation has set to promoting early diagnosis and timely management as one of the primary goals for dementia care. While a range of popular machine learning algorithms and their variants have been applied for dementia diagnosis, fuzzy systems, which have been known effective in dealing with uncertainty and offer to explicitly reason how a diagnosis can be inferred, sporadically appear in recent literature. Given the advantages of a fuzzy rule-based model, which could potentially result in a clinical decision support system that offers understandable rules and a transparent inference process to support dementia diagnosis, this paper proposes a novel fuzzy inference system by adapting the concept of dominant sets that arise from the study of graph theory. A peeling-off strategy is used to iteratively extract from the constructed edge-weighted graph a collection of dominant sets. Each dominant set is further converted into a parameterized fuzzy rule, which is finally optimized in a supervised adaptive network-based fuzzy inference framework. An illustrative example is provided that demonstrates the interpretable rules and the transparent reasoning process of reaching a decision. Further systematic experiments conducted on data from the Open Access Series of Imaging Studies (OASIS) repository, also validate its superior performance over alternative methods. |
| first_indexed | 2024-12-10T17:41:32Z |
| format | Article |
| id | doaj.art-86333a119428470ab775251f44484730 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-12-10T17:41:32Z |
| publishDate | 2022-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-86333a119428470ab775251f444847302022-12-22T01:39:22ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2022-08-011610.3389/fnins.2022.867664867664A dominant set-informed interpretable fuzzy system for automated diagnosis of dementiaTianhua Chen0Pan Su1Yinghua Shen2Lu Chen3Mufti Mahmud4Yitian Zhao5Grigoris Antoniou6Department of Computer Science, School of Computing and Engineering, University of Huddersfield, Huddersfield, United KingdomSchool of Control and Computer Engineering, North China Electric Power University, Beijing, ChinaSchool of Economics and Business Administration, Chongqing University, Chongqing, ChinaInstitute of Big Data Science and Industry, Shanxi University, Taiyuan, ChinaDepartment of Computer Science, Nottingham Trent University, Nottingham, United KingdomCixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, ChinaDepartment of Computer Science, School of Computing and Engineering, University of Huddersfield, Huddersfield, United KingdomDementia is an incurable neurodegenerative disease primarily affecting the older population, for which the World Health Organisation has set to promoting early diagnosis and timely management as one of the primary goals for dementia care. While a range of popular machine learning algorithms and their variants have been applied for dementia diagnosis, fuzzy systems, which have been known effective in dealing with uncertainty and offer to explicitly reason how a diagnosis can be inferred, sporadically appear in recent literature. Given the advantages of a fuzzy rule-based model, which could potentially result in a clinical decision support system that offers understandable rules and a transparent inference process to support dementia diagnosis, this paper proposes a novel fuzzy inference system by adapting the concept of dominant sets that arise from the study of graph theory. A peeling-off strategy is used to iteratively extract from the constructed edge-weighted graph a collection of dominant sets. Each dominant set is further converted into a parameterized fuzzy rule, which is finally optimized in a supervised adaptive network-based fuzzy inference framework. An illustrative example is provided that demonstrates the interpretable rules and the transparent reasoning process of reaching a decision. Further systematic experiments conducted on data from the Open Access Series of Imaging Studies (OASIS) repository, also validate its superior performance over alternative methods.https://www.frontiersin.org/articles/10.3389/fnins.2022.867664/fullclinical decision supportmedical diagnostic systemsdementiaAlzheimer's diseasefuzzy systemsexplainable AI |
| spellingShingle | Tianhua Chen Pan Su Yinghua Shen Lu Chen Mufti Mahmud Yitian Zhao Grigoris Antoniou A dominant set-informed interpretable fuzzy system for automated diagnosis of dementia Frontiers in Neuroscience clinical decision support medical diagnostic systems dementia Alzheimer's disease fuzzy systems explainable AI |
| title | A dominant set-informed interpretable fuzzy system for automated diagnosis of dementia |
| title_full | A dominant set-informed interpretable fuzzy system for automated diagnosis of dementia |
| title_fullStr | A dominant set-informed interpretable fuzzy system for automated diagnosis of dementia |
| title_full_unstemmed | A dominant set-informed interpretable fuzzy system for automated diagnosis of dementia |
| title_short | A dominant set-informed interpretable fuzzy system for automated diagnosis of dementia |
| title_sort | dominant set informed interpretable fuzzy system for automated diagnosis of dementia |
| topic | clinical decision support medical diagnostic systems dementia Alzheimer's disease fuzzy systems explainable AI |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2022.867664/full |
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