Your browser doesn't support javascript.
loading
Fuzzy logic based risk assessment system giving individualized advice for metabolic syndrome and fatal cardiovascular diseases.
Korkmaz, Hayriye; Canayaz, Emre; Birtane Akar, Sibel; Altikardes, Zehra Aysun.
Afiliación
  • Korkmaz H; Department of Electric and Electronics Engineering, Faculty of Technology, Marmara University, Istanbul, Turkey.
  • Canayaz E; Department of Electric and Electronics Engineering, Institute of Pure and Applied Sciences, Marmara University, Istanbul, Turkey.
  • Birtane Akar S; Department of Computer Programming, Vocational School, Istanbul Arel University, Istanbul, Turkey.
  • Altikardes ZA; Department of Computer Technologies, Vocational School of Technical Sciences, Marmara University, Istanbul, Turkey.
Technol Health Care ; 27(S1): 59-66, 2019.
Article en En | MEDLINE | ID: mdl-31045527
ABSTRACT
In 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.
Asunto(s)
Palabras clave

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Enfermedades Cardiovasculares / Lógica Difusa / Sistemas de Apoyo a Decisiones Clínicas / Síndrome Metabólico Tipo de estudio: Etiology_studies / Prognostic_studies / Risk_factors_studies Límite: Female / Humans / Male País/Región como asunto: Asia Idioma: En Revista: Technol Health Care Asunto de la revista: ENGENHARIA BIOMEDICA / SERVICOS DE SAUDE Año: 2019 Tipo del documento: Article País de afiliación: Turquía

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Enfermedades Cardiovasculares / Lógica Difusa / Sistemas de Apoyo a Decisiones Clínicas / Síndrome Metabólico Tipo de estudio: Etiology_studies / Prognostic_studies / Risk_factors_studies Límite: Female / Humans / Male País/Región como asunto: Asia Idioma: En Revista: Technol Health Care Asunto de la revista: ENGENHARIA BIOMEDICA / SERVICOS DE SAUDE Año: 2019 Tipo del documento: Article País de afiliación: Turquía