An Ontology-Based Interpretable Fuzzy Decision Support System for Diabetes Diagnosis
Open accessAbstract
Diabetes is a serious chronic disease. The importance of clinical decision support systems (CDSSs) to diagnose diabetes has led to extensive research efforts to improve the accuracy, applicability, interpretability, and interoperability of these systems. However, this problem continues to require optimization. Fuzzy rule-based systems are suitable for the medical domain, where interpretability is a main concern. The medical domain is data-intensive, and using electronic health record data to build the FRBS knowledge base and fuzzy sets is critical. Multiple variables are frequently required to determine a correct and personalized diagnosis, which usually makes it difficult to arrive at accurate and timely decisions. In this paper, we propose and implement a new semantically interpretable FRBS framework for diabetes diagnosis. The framework uses multiple aspects of knowledge-fuzzy inference, ontology reasoning, and a fuzzy analytical hierarchy process (FAHP) to provide a more intuitive and accurate design. First, we build a two-layered hierarchical and interpretable FRBS; then, we improve this by integrating an ontology reasoning process based on SNOMED CT standard ontology. We incorporate FAHP to determine the relative medical importance of each sub-FRBS. The proposed system offers numerous unique and critical improvements regarding the implementation of an accurate, dynamic, semantically intelligent, and interpretable CDSS. The designed system considers the ontology semantic similarity of diabetes complications and symptoms concepts in the fuzzy rules' evaluation process. The framework was tested using a real data set, and the results indicate how the proposed system helps physicians and patients to accurately diagnose diabetes mellitus.
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Rule-based systems were developed in two studies to provide local explanations, as they were considered closer to human reasoning and thus more preferable by clinicians.
“Rule-based systems were developed in two studies to provide local explanations, as they were considered closer to human reasoning, and thus more preferable by clinicians [124,125].”
From Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review (2021) · cites this paper as [125]
Most reviewed studies implemented a model-specific ante-hoc technique that provided local explanations.
“Most studies implemented a model-specific ante-hoc technique that provided local explanations [122,124-126,128].”
From Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review (2021) · cites this paper as [125]
El-Sappagh et al. [125] used a semantically interpretable FRBS with the integration of semantic ontology-based reasoning; the method was specific, ante-hoc, and local.
“El-Sappagh et al. [125] Semantically interpretable FRBS with the integration of semantic ontology-based reasoning specific ante-hoc local”
From Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review (2021) · cites this paper as [125]
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A Diabetes Prediction Model with Visualized Explainable Artificial Intelligence (XAI) Technology
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Distributed Probabilistic Fuzzy Rule Mining for Clinical Decision Making
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A Prototype for Diagnosis of Psoriasis in Traditional Chinese Medicine
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- Authors
- El-Sappagh, S., Alonso, J. M., Ali, F., Ali, A., Jang, J.-H., & Kwak, K.-S.
- Affiliations
- Inha University (KR)
- Venue
- IEEE Access
- Published
- 2018
- Volume
- 6
- Pages
- 37371-37394
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2874411262
- Cited by
- 79
- References
- 55
- Citations by year
- 2026: 5 · 2025: 5 · 2024: 4 · 2023: 9 · 2022: 15 · 2021: 12 · 2020: 14 · 2019: 11 · 2018: 3
- FWCI
- 12.70
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Hispana (submittedVersion)
- Repositorio institucional da Universidade de Santiago de Compostela (University of Santiago de Compostela) (submittedVersion)
- Funders
- National Research Foundation of Korea (NRF-2017R1A2B2012337)
- 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
- diabetes diagnosis, clinical decision support systems, fuzzy rule-based systems, interpretability, ontology reasoning, SNOMED CT, fuzzy analytic hierarchy process, electronic health records, diabetes mellitus, diabetes complications, semantic similarity
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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