A decision tree-based approach for identifying urban-rural differences in metabolic syndrome risk factors in the adult Korean population
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Summary
The decision-tree approach revealed that the combination of high serum triglycerides + high systolic blood pressure (SBP), high TG + low HDL cholesterol, and high waist circumference (WC) + high SBP + high fasting plasma glucose (FPG) showed high positive predictive value for the presence of MetS in the rural population.
- Type
- article
- Published
- 2012-01-30
- Cited by
- 18
- References
- 24
- OpenAlex
- https://openalex.org/W64060902
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:10907090
Keywords
Metabolic syndrome, National Cholesterol Education Program, Waist, Medicine, Population
References
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- Diagnosis and Management of the Metabolic Syndrome: An American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement
- Type 2 diabetes and metabolic syndrome in Filipina-American women : a high-risk nonobese population.
- A decision tree-based approach for determining low bone mineral density in inflammatory bowel disease using WEKA software
- Changing disease trends in the Asia-Pacific
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- Increased Prevalence of Metabolic Syndrome in Non-Obese Asian Indian—An Urban-Rural Comparison
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Cited by
- Predicting Metabolic Syndrome Using the Random Forest Method
- A review of the effects of Nigella sativa L. and its constituent, thymoquinone, in metabolic syndrome
- Clustering and combining pattern of metabolic syndrome components in a rural Brazilian adult population
- Urban, semi-urban and rural difference in the prevalence of metabolic syndrome in Shaanxi province, northwestern China: a population-based survey
- Quantitative population-health relationship (QPHR) for assessing metabolic syndrome
- Machine learning approaches for discerning intercorrelation of hematological parameters and glucose level for identification of diabetes mellitus
- Risk Factors Predicting Infectious Lactational Mastitis: Decision Tree Approach versus Logistic Regression Analysis
- Concurrent Implementation of Supervised Learning Algorithms in Disease Detection
- Pervasive Causes of Disease
- Prevention and reversal of Alzheimer's disease: treatment protocol
- Data mining for the identification of metabolic syndrome status
- Prediction of Metabolic Syndrome in a Mexican Population Applying Machine Learning Algorithms
- Predicting factors for progression of the myopia in the MiSight assessment study Spain (MASS)
- Potential value and impact of data mining and machine learning in clinical diagnostics
- Data Science in Healthcare- Current Challenges and Opportunities
- Evaluating machine learning-powered classification algorithms which utilize variants in the GCKR gene to predict metabolic syndrome: Tehran Cardio-metabolic Genetics Study
- Identifying Metabolic Syndrome Easily and Cost Effectively Using Non-Invasive Methods with Machine Learning Models
- Original article: MACHINE LEARNING APPROACHES FOR DISCERNING INTERCORRELATION OF HEMATOLOGICAL PARAMETERS AND GLUCOSE LEVEL FOR IDENTIFICATION OF DIABETES MELLITUS
- Identifying Metabolic Syndrome Easily and Cost Effectively Using Non-Invasive Methods with Machine Learning Models
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