Worachartcheewan Apilak, Shoombuatong Watshara, Pidetcha Phannee, Nopnithipat Wuttichai, Prachayasittikul Virapong, Nantasenamat Chanin
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.
ScientificWorldJournal. 2015;2015:581501. doi: 10.1155/2015/581501. Epub 2015 Jul 28.
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.
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.
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.
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.
本研究提出一种计算方法，用于确定代谢综合征（MS）的患病率，并使用美国国家胆固醇教育计划成人治疗小组第三次报告（NCEP ATP III）标准预测其发生情况。还应用随机森林（RF）方法来识别重要的健康参数。
我们使用了2008年在曼谷接受年度健康检查的5646名年龄在18至78岁之间的成年人的数据。采用NCEP ATP III标准来识别MS。应用RF方法预测MS的发生情况，并识别围绕该疾病的重要健康参数。
MS的总体患病率为23.70%（男性为34.32%，女性为17.74%）。RF预测泰国成年人群中MS的准确率为98.11%。此外，基于RF分析，甘油三酯水平是与MS相关的最重要的健康参数。
结果表明，RF预测泰国成年人群中MS的准确率>98%，且甘油三酯水平被确定为与MS相关的最具信息量的变量。因此，使用RF预测MS可能有助于识别MS状态，以预防糖尿病和心血管疾病的发生。
