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Identification of metabolic syndrome using decision tree analysis
Apilak Worachartcheewan1, Chanin Nantasenamat, Chartchalerm Isarankura-Na-Ayudhya
1Department of Clinical Microbiology, Faculty of Medical Technology, Mahidol University, Bangkok, Thailand.
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%.
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