Development of Decision Support System Using Mamdani Type Fuzzy Logic Clusters for Metabolic Syndrome Risk Assesment
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CitationBirtane, S., Canayaz, E., Altikardes, Z. A., Korkmaz, H., & Ieee. (2017). Development of Decision Support System Using Mamdani Type Fuzzy Logic Clusters for Metabolic Syndrome Risk Assesment. New York: Ieee.
Today, with the improvement of socioeconomic conditions, consumption of ready-to-eat foods increased and a more non-mobile lifestyle became widespread. Thus, the incidence of a number of metabolic problems together has begun to increase. These problems arise in the form of bodily changes, deterioration of glucose tolerance, disorders of fat metabolism and blood pressure elevation. It is called the "metabolic syndrome". The metabolic syndrome is a definition that emphasizes the importance of combining some risk factors that increase the development of cardiovascular disease and diabetes, not alone. The aim of this study is to develop a decision support system that helps the doctor working on the data obtained from the demographic and laboratory tests necessary for the diagnosis of the metabolic syndrome. By entering the data of the person through the developed user-interface; it is aimed to determine the risk factor of the person according to the analysis result. In this study, the program was run on the data of 56 patients including 34 female and 22 male, using Mamdani type fuzzy logic clusters in the LabVIEW platform.