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 paper, a data mining model is used to determine a continuous metabolic syndrome (cmets) score using 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, multilayer perceptron neural network (nn) and support vector machine (svm) models were used and statistical significance of the results was tested with 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 study showed that the most important risk factors in making cmets-lda were wc, sbp, hdl and tg for males and wc, tg, hdl and sbp for females. our research results show that high tg and central obesity have the greatest impact on mets and fbs has no effect on the final prognosis. the results 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.