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Cited by 32Artificial Intelligence in HealthcareFuzzy Logic and Control SystemsInnovation in Digital Healthcare Systems

A dynamic fuzzy rule-based inference system using fuzzy inference with semantic reasoning

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Shoaip, Nora, El-Sappagh, Shaker, Abuhmed, Tamer, Elmogy, Mohammed·2024·Scientific Reports
DOI 10.1038/s41598-024-54065-1PMC PMC10881567

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Abstract

The challenge of making flexible, standard, and early medical diagnoses is significant. However, some limitations are not fully overcome. First, the diagnosis rules established by medical experts or learned from a trained dataset prove static and too general. It leads to decisions that lack adaptive flexibility when finding new circumstances. Secondly, medical terminological interoperability is highly critical. It increases realism and medical progress and avoids isolated systems and the difficulty of data exchange, analysis, and interpretation. Third, criteria for diagnosis are often heterogeneous and changeable. It includes symptoms, patient history, demographic, treatment, genetics, biochemistry, and imaging. Symptoms represent a high-impact indicator for early detection. It is important that we deal with these symptoms differently, which have a great relationship with semantics, vary widely, and have linguistic information. This negatively affects early diagnosis decision-making. Depending on the circumstances, the diagnosis is made solo on imaging and some medical tests. In this case, although the accuracy of the diagnosis is very high, can these decisions be considered an early diagnosis or prove the condition is deteriorating? Our contribution in this paper is to present a real medical diagnostic system based on semantics, fuzzy, and dynamic decision rules. We attempt to integrate ontology semantics reasoning and fuzzy inference. It promotes fuzzy reasoning and handles knowledge representation problems. In complications and symptoms, ontological semantic reasoning improves the process of evaluating rules in terms of interpretability, dynamism, and intelligence. A real-world case study, ADNI, is presented involving the field of Alzheimer’s disease (AD). The proposed system has indicated the possibility of the system to diagnose AD with an accuracy of 97.2%, 95.4%, 94.8%, 93.1%, and 96.3% for AD, LMCI, EMCI, SMC, and CN respectively.

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  • Fuzzy logic systems allow for expert rule-based reasoning that accommodates uncertainty and vagueness, which are common characteristics in psychiatric data.

    “Fuzzy logic systems, by contrast, allow for expert rule-based reasoning that accommodates uncertainty and vagueness which are common characteristics in psychiatric data”

    From A hybrid fuzzy logic–Random Forest model to predict psychiatric treatment order outcomes: an interpretable tool for legal decision support (2025) · cites this paper as Shoaip et al., 2024

    The Fuzzy inference section says diagnosis rules are created or advised by an expert and that fuzzy inference manages imprecise knowledge through approximate reasoning. The Introduction describes uncertainty and ambiguity in medical data. The paper studies Alzheimer’s disease, however, and does not establish that uncertainty and vagueness are common characteristics of psychiatric data specifically. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • When combined with Random Forests, fuzzy logic can enhance model performance without sacrificing interpretability.

    “When combined with Random Forests, an ensemble-based machine learning method known for handling non-linear relationships and high-dimensional data, fuzzy logic can enhance model performance without sacrificing interpretability”

    From A hybrid fuzzy logic–Random Forest model to predict psychiatric treatment order outcomes: an interpretable tool for legal decision support (2025) · cites this paper as Shoaip et al., 2024

    In 'Experiment and results > Comparison with other machine learning systems' (page unavailable), the paper reports that its fuzzy inference system performed better than RandomForest on the ADNI dataset. It evaluates them as separate systems, however, and does not show that combining fuzzy logic with Random Forests improves performance or preserves interpretability. (LLM, conf=0.78)

    Experimental — accuracy not yet measured

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  1. [1]Möller H-J Graeber MB The case described by Alois Alzheimer in 1911: Historical and conceptual perspectives based on the clinical record and neurohistological sections Eur. Arch. Psychiatry Clin. Neurosci. 1998 248 111 122 10.1007/s004060050027 9728729 · DOI
  2. [3]Kim C Distinct populations of highly potent TAU seed conformers in rapidly progressing Alzheimer’s disease Sci. Transl. Med. 2022 10.1126/scitranslmed.abg0253 34985969 PMC9549419 · DOI · Paper page
  3. [4]Cammisuli DM Cipriani G Castelnuovo G Technological solutions for diagnosis, management and treatment of Alzheimer’s disease-related symptoms: A structured review of the recent scientific literature Int. J. Environ. Res. Public Health 2022 19 3122 10.3390/ijerph19053122 35270811 PMC8910738 · DOI
  4. [5]Hazan S Rapid improvement in Alzheimer’s disease symptoms following fecal microbiota transplantation: A case report J. Int. Med. Res. 2020 48 0300060520925930 10.1177/0300060520925930 32600151 PMC7328362 · DOI · Paper page
  5. [6]Vaz M Silvestre S Alzheimer’s disease: Recent treatment strategies Eur. J. Pharmacol. 2020 887 173554 10.1016/j.ejphar.2020.173554 32941929 · DOI · Paper page
  6. [7]Ossenkoppele R Research criteria for the behavioral variant of Alzheimer disease JAMA Neurol. 2022 79 48 10.1001/jamaneurol.2021.4417 34870696 PMC8649917 · DOI
  7. [8]Al-Sarayrah S Abulail D Shaalan K Understanding the impact of the ontology of semantic web in knowledge representation: A systematic review Recent Innov. Artif. Intell. Smart Appl. 2022 20 277 299
  8. [10]Bao, Q., Zhao, G., Yu, Y. & Dai, S. Ontology-based assembly process modeling with element extraction and reasoning. In CAD’21 Proceedings. 10.14733/cadconfp.2021.1-5 (CAD Solutions LLC, 2021). · DOI
  9. [11]Gomez-Valadés, A., Martínez-Tomás, R. & Rincón-Zamorano, M. Ontologies for early detection of the alzheimer disease and other neurodegenerative diseases. In International Work-Conference on the Interplay Between Natural and Artificial Computation, 42–50 (Springer, 2019).
  10. [12]Shoaip, N., Barakat, S. & Elmogy, M. Alzheimer’s disease integrated ontology (adio). In 2019 14th International Conference on Computer Engineering and Systems (ICCES), 374–379 (IEEE, 2019).
  11. [13]Shoaip N Sappagh SE Barakat S Elmogy M A framework for disease diagnosis based on fuzzy semantic ontology approach Int. J. Med. Eng. Inform. 2020 12 475 10.1504/ijmei.2020.109942 · DOI
  12. [14]Ghorbani A Davoodi F Zamanifar K Using type-2 fuzzy ontology to improve semantic interoperability for healthcare and diagnosis of depression Artif. Intell. Med. 2023 135 102452 10.1016/j.artmed.2022.102452 36628789 · DOI
  13. [16]Otte JN Beverley J Ruttenberg A BFO: Basic formal ontology1 Appl. Ontol. 2022 17 17 43 10.3233/ao-220262 · DOI
  14. [17]El-Sappagh S Franda F Ali F Kwak K-S Snomed CT standard ontology based on the ontology for general medical science BMC Med. Inform. Decis. Mak. 2018 18 1 19 10.1186/s12911-018-0651-5 30170591 PMC6119323 · DOI
  15. [18]Cross, V. & Chen, S. Fuzzy ontologies: State of the art revisited. In North American Fuzzy Information Processing Society Annual Conference, 230–242 (Springer, 2018).
  16. [20]Zhai, J., Li, M. & Zhou, K. Linguistic variable ontology and its application to fuzzy semantic retrieval. In International Conference on Information Computing and Applications, 188–195 (Springer, 2010).
  17. [21]Ghorbel, H., Bahri, A. & Bouaziz, R. Fuzzy ontologies building method: Fuzzy ontomethodology. In 2010 Annual Meeting of the North American Fuzzy Information Processing Society. 10.1109/nafips.2010.5548211 (IEEE, 2010). · DOI
  18. [22]Ktistakis IP Goodman G Shimizu C Methods for optimizing fuzzy inference systems Advances in Data Science: Methodologies and Applications 2021 Springer 97 116
  19. [23]Bobillo, F. & Straccia, U. Representing fuzzy ontologies in owl 2. In International Conference on Fuzzy Systems, 1–6 (IEEE, 2010).
  20. [24]Borgwardt S Distel F Peñaloza R The limits of decidability in fuzzy description logics with general concept inclusions Artif. Intell. 2015 218 23 55 10.1016/j.artint.2014.09.001 · DOI
  21. [25]Zekri F Ellouze AS Bouaziz R A fuzzy-based customisation of healthcare knowledge to support clinical domestic decisions for chronically ill patients J. Inf. Knowl. Manage. 2020 19 2050029 10.1142/s021964922050029x · DOI
  22. [27]Chui KT Gupta BB Alhalabi W Alzahrani FS An MRI scans-based Alzheimer’s disease detection via convolutional neural network and transfer learning Diagnostics 2022 12 1531 10.3390/diagnostics12071531 35885437 PMC9318866 · DOI
  23. [28]Liu J Alzheimer’s disease detection using depthwise separable convolutional neural networks Comput. Methods Programs Biomed. 2021 203 106032 10.1016/j.cmpb.2021.106032 33713959 · DOI
  24. [29]Savaş S Detecting the stages of Alzheimer’s disease with pre-trained deep learning architectures Arab. J. Sci. Eng. 2022 47 2201 2218 10.1007/s13369-021-06131-3 · DOI
  25. [30]El-Sappagh S Abuhmed T Islam SR Kwak KS Multimodal multitask deep learning model for Alzheimer’s disease progression detection based on time series data Neurocomputing 2020 412 197 215 10.1016/j.neucom.2020.05.087 · DOI
  26. [31]El-Sappagh S Alzheimer’s disease progression detection model based on an early fusion of cost-effective multimodal data Futur. Gener. Comput. Syst. 2021 115 680 699 10.1016/j.future.2020.10.005 · DOI
  27. [32]Sanchez, E. et al. A knowledge-based clinical decision support system for the diagnosis of alzheimer disease. In 2011 IEEE 13th International Conference on e-Health Networking, Applications and Services, 351–357 (IEEE, 2011).
  28. [33]Zhang X Hu B Ma X Moore P Chen J Ontology driven decision support for the diagnosis of mild cognitive impairment Comput. Methods Programs Biomed. 2014 113 781 791 10.1016/j.cmpb.2013.12.023 24468160 · DOI
  29. [34]Ivascu, T., Manate, B. & Negru, V. A multi-agent architecture for ontology-based diagnosis of mental disorders. In 2015 17th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), 423–430 (IEEE, 2015).
  30. [35]Toro C Using set of experience knowledge structure to extend a rule set of clinical decision support system for Alzheimer’s disease diagnosis Cybern. Syst. 2012 43 81 95 10.1080/01969722.2012.654070 · DOI
  31. [37]Shoaip N A comprehensive fuzzy ontology-based decision support system for Alzheimer’s disease diagnosis IEEE Access 2020 9 31350 31372 10.1109/ACCESS.2020.3048435 · DOI
  32. [38]Bangyal WH Constructing domain ontology for Alzheimer disease using deep learning based approach Electronics 2022 11 1890 10.3390/electronics11121890 · DOI
  33. [39]Benson GS Don’t forget about tau: The effects of apoe4 genotype on Alzheimer’s disease cerebrospinal fluid biomarkers in subjects with mild cognitive impairment-data from the dementia competence network J. Neural Transm. 2022 20 1 10 PMC9188507 35061102
  34. [40]Cersonsky TE Using the Montreal cognitive assessment to identify individuals with subtle cognitive decline Neuropsychology 2022 20 20 PMC9912279 35511561
  35. [41]Huang H-C Tseng Y-M Chen Y-C Chen P-Y Chiu H-Y Diagnostic accuracy of the clinical dementia rating scale for detecting mild cognitive impairment and dementia: A bivariate meta-analysis Int. J. Geriatr. Psychiatry 2021 36 239 251 10.1002/gps.5436 32955146 · DOI
  36. [42]Arevalo-Rodriguez I Mini-mental state examination (MMSE) for the early detection of dementia in people with mild cognitive impairment (MCI) Cochrane Database Syst. Rev. 2021 20 20 PMC8406467 34313331
  37. [43]van Loenhoud AC Cognitive reserve and clinical progression in Alzheimer disease: A paradoxical relationship Neurology 2019 93 e334 e346 31266904 PMC6669930
  38. [45]Putcha D Fractionating the rey auditory verbal learning test: Distinct roles of large-scale cortical networks in prodromal Alzheimer’s disease Neuropsychologia 2019 129 83 92 10.1016/j.neuropsychologia.2019.03.015 30930301 PMC6571167 · DOI
  39. [46]Fokuoh E Longitudinal analysis of apoe-ε4 genotype with the logical memory delayed recall score in Alzheimer’s disease J. Genet. 2021 100 1 9 10.1007/s12041-021-01309-y 34470924 · DOI
  40. [47]Eroglu Y Yildirim M Cinar A MRMR-based hybrid convolutional neural network model for classification of Alzheimer’s disease on brain magnetic resonance images Int. J. Imaging Syst. Technol. 2022 32 517 527 10.1002/ima.22632 · DOI
  41. [48]Yang Z Liu Z The risk prediction of Alzheimer’s disease based on the deep learning model of brain 18f-fdg positron emission tomography Saudi J. Biol. Sci. 2020 27 659 665 10.1016/j.sjbs.2019.12.004 32210685 PMC6997895 · DOI
  42. [49]Cingolani P Alcalá-Fdez J jfuzzylogic: A java library to design fuzzy logic controllers according to the standard for fuzzy control programming Int. J. Comput. Intell. Syst. 2013 6 61 75 10.1080/18756891.2013.818190 · DOI

Cited by

Sorted by citations

Authors
Shoaip, N., El-Sappagh, S., Abuhmed, T., & Elmogy, M.
Affiliations
  • Damanhour University (EG)
  • Sungkyunkwan University (KR)
  • Mansoura University (EG)
Corresponding author
  • Abuhmed, Tamer
  • Elmogy, Mohammed
ORCID
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2024-02-21
Volume
14
Issue
1
Pages
4275-4275
ISSN
2045-2322 (ISSN-L)
DOI
10.1038/s41598-024-54065-1
PMC
PMC10881567
OpenAlex
W4391997998
Cited by
32
References
1
Citations by year
2026: 14 · 2025: 12 · 2024: 6
FWCI
9.69
Type
journal article
Language
en
Open access
Licence
Locations
  • Scientific Reports (publishedVersion, cc-by)
  • Research Square (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
  • Ministry of Science and ICT, South Korea (IITP-2021-2020-0-01821)
  • National Research Foundation of Korea (NRF) grant (2021R1A2C1011198)
Topics
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Physical Sciences › Computer Science › Artificial Intelligence › Fuzzy Logic and Control Systems
  • Health Sciences › Health Professions › Health Information Management › Innovation in Digital Healthcare Systems
Keywords
fuzzy inference, semantic reasoning, ontology reasoning, diagnosis, early diagnosis, Alzheimer's disease, ADNI, symptom-based diagnosis, knowledge representation, mild cognitive impairment
MeSH terms
Alzheimer Disease, Humans, Linguistics, Problem Solving, Semantics, Fuzzy Logic

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