Identification of metabolic syndrome using decision tree analysis

Apilak Worachartcheewan; Chanin Nantasenamat; Chartchalerm Isarankura-Na-Ayudhya; Phannee Pidetcha; Virapong Prachayasittikul

doi:10.1016/j.diabres.2010.06.009

Identification of metabolic syndrome using decision tree analysis

Diabetes Res Clin Pract. 2010 Oct;90(1):e15-8. doi: 10.1016/j.diabres.2010.06.009.

Authors

Apilak Worachartcheewan¹, Chanin Nantasenamat, Chartchalerm Isarankura-Na-Ayudhya, Phannee Pidetcha, Virapong Prachayasittikul

Affiliation

¹ Department of Clinical Microbiology, Faculty of Medical Technology, Mahidol University, Bangkok, Thailand.

PMID: 20619912
DOI: 10.1016/j.diabres.2010.06.009

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%.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Artificial Intelligence
Data Mining
Decision Making, Computer-Assisted*
Decision Trees*
Humans
Medical Records
Metabolic Syndrome / blood
Metabolic Syndrome / diagnosis*
Metabolic Syndrome / epidemiology
Predictive Value of Tests
Risk Factors
Thailand / epidemiology