Integration of the Wang & Mendel Algorithm into the Application of Fuzzy Expert Systems to Intelligent Clinical Decision Support Systems

Manuel Casal-Guisande (), Jorge Cerqueiro-Pequeño, José-Benito Bouza-Rodríguez and Alberto Comesaña-Campos ()
Additional contact information
Manuel Casal-Guisande: Department of Design in Engineering, University of Vigo, 36208 Vigo, Spain
Jorge Cerqueiro-Pequeño: Department of Design in Engineering, University of Vigo, 36208 Vigo, Spain
José-Benito Bouza-Rodríguez: Department of Design in Engineering, University of Vigo, 36208 Vigo, Spain
Alberto Comesaña-Campos: Department of Design in Engineering, University of Vigo, 36208 Vigo, Spain

Mathematics, 2023, vol. 11, issue 11, 1-33

Abstract: The use of intelligent systems in clinical diagnostics has evolved, integrating statistical learning and knowledge-based representation models. Two recent works propose the identification of risk factors for the diagnosis of obstructive sleep apnea (OSA). The first uses statistical learning to identify indicators associated with different levels of the apnea-hypopnea index (AHI). The second paper combines statistical and symbolic inference approaches to obtain risk indicators ( Statistical Risk and Symbolic Risk ) for a given AHI level. Based on this, in this paper we propose a new intelligent system that considers different AHI levels and generates risk pairs for each level. A learning-based model generates Statistical Risks based on objective patient data, while a cascade of fuzzy expert systems determines a Symbolic Risk using symptom data from patient interviews. The aggregation of risk pairs at each level involves a fuzzy expert system with automatically generated fuzzy rules using the Wang-Mendel algorithm. This aggregation produces an Apnea Risk indicator for each AHI level, allowing discrimination between OSA and non-OSA cases, along with appropriate recommendations. This approach improves variability, usefulness, and interpretability, increasing the reliability of the system. Initial tests on data from 4400 patients yielded AUC values of 0.74–0.88, demonstrating the potential benefits of the proposed intelligent system architecture.

Keywords: design; machine learning; expert systems; fuzzy logic; automatic rule generation; information fusion; intelligent system; decision-making; Wang–Mendel (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2227-7390/11/11/2469/pdf (application/pdf)
https://www.mdpi.com/2227-7390/11/11/2469/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:11:y:2023:i:11:p:2469-:d:1157360

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().