Comput Math Methods Med - Use of CHAID decision trees to formulate pathways for the early detection of metabolic syndrome in young adults.

Tópicos

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{ data(2317) use(1299) case(1017) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
{ gene(2352) biolog(1181) express(1162) }
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Resumo

Metabolic syndrome (MetS) in young adults (age 20-39) is often undiagnosed. A simple screening tool using a surrogate measure might be invaluable in the early detection of MetS. Methods. A chi-squared automatic interaction detection (CHAID) decision tree analysis with waist circumference user-specified as the first level was used to detect MetS in young adults using data from the National Health and Nutrition Examination Survey (NHANES) 2009-2010 Cohort as a representative sample of the United States population (n = 745). Results. Twenty percent of the sample met the National Cholesterol Education Program Adult Treatment Panel III (NCEP) classification criteria for MetS. The user-specified CHAID model was compared to both CHAID model with no user-specified first level and logistic regression based model. This analysis identified waist circumference as a strong predictor in the MetS diagnosis. The accuracy of the final model with waist circumference user-specified as the first level was 92.3% with its ability to detect MetS at 71.8% which outperformed comparison models. Conclusions. Preliminary findings suggest that young adults at risk for MetS could be identified for further followup based on their waist circumference. Decision tree methods show promise for the development of a preliminary detection algorithm for MetS.

Resumo Limpo

metabol syndrom met young adult age often undiagnos simpl screen tool use surrog measur might invalu earli detect met method chisquar automat interact detect chaid decis tree analysi waist circumfer userspecifi first level use detect met young adult use data nation health nutrit examin survey nhane cohort repres sampl unit state popul n result twenti percent sampl met nation cholesterol educ program adult treatment panel iii ncep classif criteria met userspecifi chaid model compar chaid model userspecifi first level logist regress base model analysi identifi waist circumfer strong predictor met diagnosi accuraci final model waist circumfer userspecifi first level abil detect met outperform comparison model conclus preliminari find suggest young adult risk met identifi followup base waist circumfer decis tree method show promis develop preliminari detect algorithm met

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