Publication: Predicting metabolic syndrome using the random forest method
3
Issued Date
2015-01-01
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ISSN
1537744X
23566140
23566140
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2-s2.0-84939202992
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Mahidol University
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SCOPUS
Bibliographic Citation
Scientific World Journal. Vol.2015, (2015)
Suggested Citation
Apilak Worachartcheewan, Watshara Shoombuatong, Phannee Pidetcha, Wuttichai Nopnithipat, Virapong Prachayasittikul, Chanin Nantasenamat Predicting metabolic syndrome using the random forest method. Scientific World Journal. Vol.2015, (2015). doi:10.1155/2015/581501 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/35513
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Title
Predicting metabolic syndrome using the random forest method
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Abstract
© 2015 Apilak Worachartcheewan et al. 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.