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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Ozet{:e-Bu yah�mada, arama ve optimizasyon yah�malannda genel olarak en slk kullamlan genetik algoritma yiintemi ile lizerinde yogun yah�llan karma�lk problemlerin ba�mda gelen gezgin satlcl probleminin farkh bir modeli olan asimetrik gezgin satlcl probleminin yiizlim yakla�lml anlatllmaktadlr. Onerilen sistemin test sonuylan ortaya konulmu� ve sistemin kabul edilebilir slirelerde etkin bir yiizlim ortaml sagladlgl giisterilmi�tir.Abstract-This study describes the genetic algorithm method that is most commonly used in search and optimization studies with solution approach of the asymmetric travelling salesman problem, which is the leading problem of the complex problems and a different model of the traveling salesman problem. The proposed system has been put forward the test results and the system has been shown to provide an acceptable period of time with an effective solution environment.