Development and Validation of Metabolic Syndrome Prediction and Classification-Pathways using Decision Trees
|Brian Miller1* and Mark Fridline2|
|1School of Sport Science & Wellness Education, The University of Akron, Akron, OH; Doctoral Student, Health Education and Promotion, School of Health Sciences, Kent State University, Kent, OH, USA|
|2Department of Statistics, The University of Akron, Akron, OH, USA|
|Corresponding Author :||Brian Miller
School of Sport Science & Wellness Education InfoCision Stadium 317
The University of Akron, Akron
OH, 44325-5103, USA
Tel: (216) 659-6985
E-mail: [email protected]
|Received December 16, 2014; Accepted January 05, 2015; Published January 10, 2015|
|Citation: Miller B, Fridline M (2015) Development and Validation of Metabolic Syndrome Prediction and Classification-Pathways using Decision Trees. J Metabolic Synd 4:173. doi:10.4172/2167-0943.1000173|
|Copyright: © 2015 Miller B, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.|
Purpose: The purpose of the current investigation was to create, compare, and validate sex-specific decision tree models to classify metabolic syndrome.
Methods: Sex-specific Chi-Squared Automatic Interaction Detection, Exhaustive Chi-Squared Automatic
Interaction Detection, and Classification and Regression Tree algorithms were run in duplicate using metabolic syndrome classification criteria, subject characteristics, and cardiovascular predictor variable from the National Health and Nutrition Examination Survey cohort data. Data from 1999-2012 were used (n=10,639; 1999-2010 cohorts for model creation and 2011-2012 cohort for model validation). Metabolic Syndrome was classified as the presence of 3 of 5 American Heart Association National Heart Lung and Blood Institute Metabolic Syndrome classification criteria. The first run was made with all predictor variables and the second run was made excluding metabolic syndrome classification predictor variables. Given that the included decision tree algorithms are non-parametric procedures, all decision tree models were compared to a logistic regression based model to provide a parametric comparison.
Results: The Classification and Regression Tree algorithm outperformed all other decision tree models and logistic regression with a specificity of 0.908 and 0.952, sensitivity of 0.896 and 0.848, and misclassification error of 0.096 and 0.080 for males and females, respectively. Only one predictor variable outside of the metabolic syndrome classification reached significance in the female model (age). All metabolic syndrome classification predictor variables reached significance in the male model. Waist circumference did not reach significance in the female model. Within each model, 5 female and 3 male pathways built off of <3 American Heart Association National Heart Lung and Blood Institute Metabolic Syndrome classification criteria resulted in an increased likelihood of presenting Metabolic Syndrome.
Conclusion: The proposed pathways show promise over other current metabolic syndrome classification
models in identifying Metabolic Syndrome with <3 predictor variables, before current classification criteria.