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The Scientific World Journal
Volume 2015, Article ID 581501, 10 pages
http://dx.doi.org/10.1155/2015/581501
Research Article

Predicting Metabolic Syndrome Using the Random Forest Method

1Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand
2Department of Clinical Chemistry, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand
3Excellence Service Center for Medical Technology and Quality Improvement, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand
4Department of Clinical Microbiology and Applied Technology, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand

Received 25 February 2015; Revised 4 June 2015; Accepted 7 June 2015

Academic Editor: Naval Vikram

Copyright © 2015 Apilak Worachartcheewan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [4 citations]

The following is the list of published articles that have cited the current article.

  • Habeebah Adamu Kakudi, Chu Kiong Loo, and Foong Ming Moy, “Predicting metabolic syndrome using risk quantification and ensemble methods,” 2017 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1–8, . View at Publisher · View at Google Scholar
  • Maryam Tayefi, Maryam Saberi-Karimian, Habibollah Esmaeili, Alireza Amirabadi Zadeh, Mahmoud Ebrahimi, Mohsen Mohebati, Alireza Heidari-Bakavoli, Mahmoud Reza Azarpajouh, Masoud Heshmati, Mohammad Safarian, Mohsen Nematy, Seyed Mohammad Reza Parizadeh, Gordon A. Ferns, and Majid Ghayour-Mobarhan, “Evaluating of associated risk factors of metabolic syndrome by using decision tree,” Comparative Clinical Pathology, 2017. View at Publisher · View at Google Scholar
  • Ioannis Kavakiotis, Olga Tsave, Athanasios Salifoglou, Nicos Maglaveras, Ioannis Vlahavas, and Ioanna Chouvarda, “Machine Learning and Data Mining Methods in Diabetes Research,” Computational and Structural Biotechnology Journal, 2017. View at Publisher · View at Google Scholar
  • Virapong Prachayasittikul, Apilak Worachartcheewan, Nalini Schaduangrat, and Chanin Nantasenamat, “Data mining for the identification of metabolic syndrome status,” EXCLI Journal, vol. 17, pp. 72–88, 2018. View at Publisher · View at Google Scholar