Identification of metabolic syndrome using decision tree analysis.
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%.
Worachartcheewan
Apilak
A
Department of Clinical Microbiology, Faculty of Medical Technology, Mahidol University, Bangkok, Thailand.
Nantasenamat
Chanin
C
Isarankura-Na-Ayudhya
Chartchalerm
C
Pidetcha
Phannee
P
Prachayasittikul
Virapong
V
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