Predicting Metabolic Syndrome Using the Random Forest Method.
OBJECTIVE
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.


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.


CONCLUSIONS
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.
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.
Apilak
A
Worachartcheewan
Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.
Watshara
W
Shoombuatong
Excellence Service Center for Medical Technology and Quality Improvement, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.
Phannee
P
Pidetcha
Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.
Wuttichai
W
Nopnithipat
Department of Clinical Microbiology and Applied Technology, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.
Virapong
V
Prachayasittikul
Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand.
Chanin
C
Nantasenamat
D000293:Adolescent; D000328:Adult; D000368:Aged; D015415:Biomarkers; D005260:Female; D006801:Humans; D008297:Male; D024821:Metabolic Syndrome / Q000175:diagnosis / Q000453:epidemiology*; D008875:Middle Aged; D015233:Models, Statistical; D015995:Prevalence; D011379:Prognosis; D015203:Reproducibility of Results; D013785:Thailand / Q000453:epidemiology; D014505:Urban Population; D055815:Young Adult
D015415:Biomarkers