Apilak WorachartcheewanChanin NantasenamatChartchalerm Isarankura-Na-AyudhyaPhannee PidetchaVirapong PrachayasittikulMahidol University2018-09-242018-09-242010-10-01Diabetes Research and Clinical Practice. Vol.90, No.1 (2010)016882272-s2.0-77956191875https://repository.li.mahidol.ac.th/handle/123456789/28621This study employs decision tree as a decision support system for rapid and automated identification of individuals with metabolic syndrome (MS) among a Thai population. Results demonstrated strong predictivity of the decision tree in classification of individuals with and without MS, displaying an overall accuracy in excess of 99%. © 2010 Elsevier Ireland Ltd.Mahidol UniversityBiochemistry, Genetics and Molecular BiologyMedicineArticleSCOPUS10.1016/j.diabres.2010.06.009