Developing a Novel Continuous Metabolic Syndrome Score: A Data Mining Based Model

Today, metabolic syndrome in the age group of children and adolescents has become a global concern. In this work, a data mining model is used in order to determine a continuous metabolic syndrome (cMetS) score using the linear discriminate analysis (cMetS-LDA). The decision tree model is used to specify the calculated optimal cut-off point cMetS-LDA. In order to evaluate the method, the multi-layer perceptron neural network (NN) and the support vector machine (SVM) models are used, and the statistical significance of the results is tested with the Wilcoxon signed-rank test. According to the results of this test, the proposed CART is significantly better than the NN and SVM models. The ranking results in this work show that the most important risk factors in making cMetS-LDA are WC, SBP, HDL, and TG for the males, and WC, TG, HDL, and SBP for the females. Our research work results show that the high TG and central obesity have the greatest impacts on MetS, and that FBS has no effect on the final prognosis. The results obtained also indicate that in the preliminary stages of MetS, WC, HDL, and SBP are the most important influencing factors that play an important role in forecasting.