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dc.contributor.authorKorkmaz, Hayriyeen_US
dc.contributor.authorCanayaz, Emreen_US
dc.contributor.authorBirtane, Sibelen_US
dc.contributor.authorAltikardes, Aysunen_US
dc.date.accessioned2019-10-29T17:31:59Z
dc.date.available2019-10-29T17:31:59Z
dc.date.issued2019
dc.identifier.issn0928-7329
dc.identifier.issn1878-7401
dc.identifier.urihttps://dx.doi.org/10.3233/THC-199007
dc.identifier.urihttps://hdl.handle.net/20.500.12294/1699
dc.description7th International Conference on Biomedical Engineering and Biotechnology (iCBEB) --OCT 17-20, 2018 -- Nanjing, PEOPLES R CHINAen_US
dc.descriptionBirtane, Sibel (Arel Author) --- WOS: 000472616700007en_US
dc.descriptionPubMed ID: 31045527en_US
dc.description.abstractIn 2005, global cardiovascular diseases caused 30% of deaths in Europe, which is 46% of total deaths for all death groups. Today, according to the International Adult Diabetes Federation, 20% to 25% of the adult population in the world has Metabolic Syndrome. Turkish Statistical Institute claims that in Turkey 408782 people died of circulatory system diseases in 2016 and it is expected that numbers will dramatically increase. In 2003, total worldwide healthcare budget of Diabetes Mellitus was up to 64.9 billion International Dollars with the continuing rise in prevalence, it is expected that total costs will increase to 396 billion International Dollars by 2025. The main purpose of this study was to present a clinical decision support system that calculates Metabolic Syndrome existence and evaluate HeartScore risk level for Turkish population. The second objective was to create a detailed personal report about individual's risk level of Metabolic Syndrome and HeartScore and give advice to him/her to reduce it. The fuzzy logic risk assessment system (FLRAS) was formed in LabVIEW graphical development platform according to International Diabetes Federation and European Heart Journal's criteria. Mamdani type fuzzy logic sets were identified for each input variable and membership functions were assigned depending on the magnitude of the input limits. System's performance was tested on 96 (72 females, 24 males) patient data. Results show that the proposed system was able to evaluate the Metabolic Syndrome risk with 0.9285 specificity, 0.92708 accuracy and 0.925 sensitivity.en_US
dc.language.isoengen_US
dc.publisherIOS PRESSen_US
dc.relation.ispartofTECHNOLOGY AND HEALTH CAREen_US
dc.identifier.doi10.3233/THC-199007en_US
dc.identifier.doi10.3233/THC-199007
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectFuzzy Logicen_US
dc.subjectDecision Support Systemen_US
dc.subjectAutomatic Report Generationen_US
dc.subjectIndividualized Medical Advicesen_US
dc.titleFuzzy logic based risk assessment system giving individualized advice for metabolic syndrome and fatal cardiovascular diseasesen_US
dc.typearticleen_US
dc.departmentİstanbul Arel Üniversitesi, Meslek Yüksekokulu, Bilgisayar Programcılığı Programıen_US
dc.identifier.volume27en_US
dc.identifier.startpageS59en_US
dc.identifier.endpageS66en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.department-temp[Korkmaz, Hayriye] Marmara Univ, Fac Technol, Dept Elect & Elect Engn, Istanbul, Turkey -- [Canayaz, Emre] Marmara Univ, Inst Pure & Appl Sci, Dept Elect & Elect Engn, Istanbul, Turkey -- [Birtane, Sibel] Istanbul Arel Univ, Istanbul, Turkey -- [Altikardes, Aysun] Marmara Univ, Vocat Sch Tech Sci, Comp Programming Technol Dept, Istanbul, Turkeyen_US


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