使用随机森林方法预测代谢综合征

Predicting Metabolic Syndrome Using the Random Forest Method.

作者信息

Worachartcheewan Apilak, Shoombuatong Watshara, Pidetcha Phannee, Nopnithipat Wuttichai, Prachayasittikul Virapong, Nantasenamat Chanin

机构信息

Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand ; Department of Clinical Chemistry, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.

Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.

出版信息

ScientificWorldJournal. 2015;2015:581501. doi: 10.1155/2015/581501. Epub 2015 Jul 28.

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.