title: Identification of metabolic syndrome using decision tree analysis.
abstract: This 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%.
authors.affiliation: Department of Clinical Microbiology, Faculty of Medical Technology, Mahidol University, Bangkok, Thailand.
authors.forename: Apilak
authors.initials: A
authors.lastname: Worachartcheewan
authors.forename: Chanin
authors.initials: C
authors.lastname: Nantasenamat
authors.forename: Chartchalerm
authors.initials: C
authors.lastname: Isarankura-Na-Ayudhya
authors.forename: Phannee
authors.initials: P
authors.lastname: Pidetcha
authors.forename: Virapong
authors.initials: V
authors.lastname: Prachayasittikul
mesh_terms: D001185:Artificial Intelligence; D057225:Data Mining; D003658:Decision Making, Computer-Assisted; D003663:Decision Trees; D006801:Humans; D008499:Medical Records; D024821:Metabolic Syndrome / Q000097:blood / Q000175:diagnosis* / Q000453:epidemiology; D011237:Predictive Value of Tests; D012307:Risk Factors; D013785:Thailand / Q000453:epidemiology