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Title: Predicting Metabolic Syndrome Using the Random Forest Method
Authors: Apilak Worachartcheewan
Watshara Shoombuatong
Phannee Pidetcha
Wuttichai Nopnithipat
Virapong Prachayasittikul
Chanin Nantasenamat
Mahidol University. Faculty of Medical Technology. Center of Data Mining and Biomedical Informatics
Mahidol University. Faculty of Medical Technology. Department of Clinical Microbiology and Applied Technology
Keywords: Metabolic Syndrome;Forest Method
Issue Date: 7-Jun-2015
Citation: The Scientific World Journal. 2015, ID 581501
Abstract: Aims. This study proposes a computational method for determining the prevalence of metabolic syndrome (MS) and to predict its occurrence using the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) criteria. The Random Forest (RF) method is also applied to identify significant health parameters. Materials and Methods. We used data from 5,646 adults aged between 18–78 years residing in Bangkok who had received an annual health check-up in 2008. MS was identified using the NCEP ATP III criteria. The RF method was applied to predict the occurrence of MS and to identify important health parameters surrounding this disorder. Results. The overall prevalence of MS was 23.70% (34.32% for males and 17.74% for females). RF accuracy for predicting MS in an adult Thai population was 98.11%. Further, based on RF, triglyceride levels were the most important health parameter associated with MS. Conclusion. RF was shown to predict MS in an adult Thai population with an accuracy >98% and triglyceride levels were identified as the most informative variable associated with MS. Therefore, using RF to predict MS may be potentially beneficial in identifying MS status for preventing the development of diabetes mellitus and cardiovascular diseases.
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