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Fuzzy logic based risk assessment system giving individualized advice for metabolic syndrome and fatal cardiovascular diseases

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 tha...

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Autores principales: Korkmaz, Hayriye, Canayaz, Emre, Birtane, Sibel, Altıkardeş, Aysun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: IOS Press 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6598019/
https://www.ncbi.nlm.nih.gov/pubmed/31045527
http://dx.doi.org/10.3233/THC-199007
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author Korkmaz, Hayriye
Canayaz, Emre
Birtane, Sibel
Altıkardeş, Aysun
author_facet Korkmaz, Hayriye
Canayaz, Emre
Birtane, Sibel
Altıkardeş, Aysun
author_sort Korkmaz, Hayriye
collection PubMed
description 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.
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spelling pubmed-65980192019-07-01 Fuzzy logic based risk assessment system giving individualized advice for metabolic syndrome and fatal cardiovascular diseases Korkmaz, Hayriye Canayaz, Emre Birtane, Sibel Altıkardeş, Aysun Technol Health Care Research Article 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. IOS Press 2019-06-18 /pmc/articles/PMC6598019/ /pubmed/31045527 http://dx.doi.org/10.3233/THC-199007 Text en © 2019 – IOS Press and the authors. All rights reserved https://creativecommons.org/licenses/by-nc/4.0/ This article is published online with Open Access and distributed under the terms of the Creative Commons Attribution Non-Commercial License (CC BY-NC 4.0).
spellingShingle Research Article
Korkmaz, Hayriye
Canayaz, Emre
Birtane, Sibel
Altıkardeş, Aysun
Fuzzy logic based risk assessment system giving individualized advice for metabolic syndrome and fatal cardiovascular diseases
title Fuzzy logic based risk assessment system giving individualized advice for metabolic syndrome and fatal cardiovascular diseases
title_full Fuzzy logic based risk assessment system giving individualized advice for metabolic syndrome and fatal cardiovascular diseases
title_fullStr Fuzzy logic based risk assessment system giving individualized advice for metabolic syndrome and fatal cardiovascular diseases
title_full_unstemmed Fuzzy logic based risk assessment system giving individualized advice for metabolic syndrome and fatal cardiovascular diseases
title_short Fuzzy logic based risk assessment system giving individualized advice for metabolic syndrome and fatal cardiovascular diseases
title_sort fuzzy logic based risk assessment system giving individualized advice for metabolic syndrome and fatal cardiovascular diseases
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6598019/
https://www.ncbi.nlm.nih.gov/pubmed/31045527
http://dx.doi.org/10.3233/THC-199007
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