Designing a Fuzzy Expert System for Diagnosis and Prediction of Metabolic Syndrome in Children and Adolescents

Introduction: Metabolic Syndrome (MetS) is one of the most common metabolic disordersseen in children and adolescents. In this study, the prevalence of MetS and its related factorsare evaluated using a fuzzy expert system (FES) in a national representative sample of agegroups.Methods: The FES is designed based on the data of 800 participants of the fifth study of theprogram for monitoring and prevention of non-communicable diseases among children andadolescents in Iran in 2015. The data of 560 participants were used as training data and 240 astest data were used to test the rules and output of the system. The fuzzy system that has beendesigned includes input data (age, waist, systolic blood pressure, diastolic blood pressure,BMI, waist-to-height ratio, nutrition, and abdominal obesity), and at the end gives us anoutput that diagnoses the health status with MetS or predicts the disease. Results: The analysis shows that this method, with an accuracy of more than 98%, can predictand diagnose MetS among children and adolescents better than other methods.Conclusion: The fuzzy system is designed to accept multiple variables simultaneously asinput variables and also use more people information than similar research as primary data.In addition, its accuracy is more than 98%. Preliminary data were collected from children andadolescents with different lifestyles across the country. This system can act as an assistant inthe service of a specialist doctor to diagnose the disease.