Selection of Aggregation Function in Fuzzy Inference System for Metabolic Syndrome

Abstract

Metabolic syndrome (MetS) has long-term, very detrimental effects, including chronic kidney disease, cardiovascular disease, stroke, and diabetes mellitus. Therefore, early detection of MetS is very important. Numerous global health organizations have made some Metabolic Syndrome (MetS) diagnosis criteria, but they are still mostly in a dichotomous form. On the other hand, a continuous MetS risk score has been proven to be more sensitive and with less risk of error. This study aims to build a Fuzzy Inference System (FIS) model. MetS diagnostic criteria issued by NCEP-APT III are used as a reference for generating rules. This model uses max, probor, and additive functions to obtain membership values as a result of rules aggregation in seven steps: 1) Identification of variables; 2) Determination of fuzzy sets and their membership functions; 3) Knowledge base generation; 4) Implementation of the implication functions; 5) Fuzzy rules aggregation; 6) Defuzzification; 7) Performance testing of the model and selecting the best aggregation function. The findings show the max function as the most suitable function for the aggregation process with an accuracy, sensitivity, specificity, and precision value of 100% according to the measurement results with NCEP-ATP III. A continuous risk score between 0% and 99.99% is considered a non-high risk, whereas a score of 100% indicates a high risk. This function also has an ideal risk value distribution according to the neighborhood level of the NCEP-ATP III diagnostic criteria.