A Hybrid Fuzzy Rule-Based Polyhedral Separation Approach: Medical Diagnosis Application

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dc.contributor.authorAyaz, Halil Ibrahim
dc.contributor.authorErvural, Bilal
dc.date.accessioned2024-02-23T13:39:00Z
dc.date.available2024-02-23T13:39:00Z
dc.date.issued2022
dc.departmentNEÜen_US
dc.description4th International Conference on Intelligent and Fuzzy Systems (INFUS) -- JUL 19-21, 2022 -- Bornova, TURKEYen_US
dc.description.abstractDiscrimination of two linearly inseparable sets using hyperplane(s) is considered one of the most successful classification methods. There is an expanding body of literature that realize the significance of classification of data accurately. Particularly, medical applications of classification provide meaningful early diagnosis results. However, medical datasets in real-world applications possess some noise and uncertainty. Therefore, dealing with uncertainty is a critical factor for accurate diagnosis. To overcome mentioned drawbacks, this study presents a hybrid fuzzy rule-based robust linear programming (RLP) and h-polyhedral separation (h-PolSep) approaches for breast cancer diagnosis through several consequent stages. In the proposed two-stage model, firstly the data is relabeled according to the fuzzy rule-based system, then the new outputs and original input values are classified using RLP and h-PolSep methods. Input fuzzification, generating membership functions, extracting fuzzy rules, and output defuzzification are examined in detail. A frequently used real-world medical dataset from the UCI Repository: the Wisconsin breast cancer is employed to evaluate the effectiveness of the classification using a number of metrics. Finally, the results demonstrate that the proposed approach effectively handles medical data classification problems.en_US
dc.identifier.doi10.1007/978-3-031-09173-5_10
dc.identifier.endpage81en_US
dc.identifier.isbn978-3-031-09173-5
dc.identifier.isbn978-3-031-09172-8
dc.identifier.issn2367-3370
dc.identifier.issn2367-3389
dc.identifier.scopus2-s2.0-85135017232en_US
dc.identifier.scopusqualityQ4en_US
dc.identifier.startpage73en_US
dc.identifier.urihttps://doi.org/10.1007/978-3-031-09173-5_10
dc.identifier.urihttps://hdl.handle.net/20.500.12452/10671
dc.identifier.volume504en_US
dc.identifier.wosWOS:000889380800010en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer International Publishing Agen_US
dc.relation.ispartofIntelligent And Fuzzy Systems: Digital Acceleration And The New Normal, Infus 2022, Vol 1en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectFuzzy Logicen_US
dc.subjectClassificationen_US
dc.subjectMedical Dataen_US
dc.subjectPolyhedral Separabilityen_US
dc.titleA Hybrid Fuzzy Rule-Based Polyhedral Separation Approach: Medical Diagnosis Applicationen_US
dc.typeConference Objecten_US

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