Authors: Worachartcheewan, Apilak
Nantasenamat, Chanin
Isarankura-Na-Ayudhya, Chartchalerm
Prachayasittikul, Virapong
Title: Quantitative population-health relationship (QPHR) for assessing metabolic syndrome
Language (ISO): en
Abstract: Background: Metabolic syndrome (MS) is a condition that predisposes individuals to the development of cardiovascular diseases and type 2 diabetes mellitus. Methods: A cross-sectional investigation of 15,365 participants residing in metropolitan Bangkok who had received an annual health checkup in 2007 was used in this study. Individuals were classified as MS or non-MS according to the International Diabetes Federation criteria using BMI cutoff of ≥ 25 kg/m2 plus two or more MS components. This study explores the utility of quantitative population-health relationship (QPHR) for predicting MS status as well as discovers variables that frequently occur together. The former was achieved by decision tree (DT) analysis, artificial neural network (ANN), support vector machine (SVM) and principal component analysis (PCA) while the latter was obtained by association analysis(AA). Results: DT outperformed both ANN and SVM in MS classification as deduced from its accuracy value of 99 % as compared to accuracies of 98 % and 91 % for ANN and SVM, respectively. Furthermore, PCA was able to effectively classify individuals as MS and non-MS as observed from the scores plot. Moreover, AA was employed to analyze individuals with MS in order to elucidate pertinent rule from MS components that occur frequently together, which included TG+BP, BP+FPG and TG+FPG where TG, BP and FPG corresponds to triglyceride, blood pressure and fasting plasma glucose, respectively. Conclusion: QPHR was demonstrated to be useful in predicting the MS status of individuals from an urban Thai population. Rules obtained from AA analysis provided general guidelines (i.e. co-occurrences of TG, BP and FPG) that may be used in the prevention of MS in at risk individuals.
Subject Headings: metabolic syndrome
cardiovascular diseases
data mining
Issue Date: 2013-06-26
Appears in Collections:Original Articles

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