Deriving rules for identifying diabetic among individuals with metabolic syndrome
2
조회
0
다운로드
2
인용
0
참고문헌
키워드
초록
The objective of this study is to derive specific classification rules that could be used to prevent individuals with Metabolic Syndrome (MS) from developing diabetes. Specifically, we aim to identify rules which classify individuals with MS into those without diabetes (class 0) and those with diabetes (class 1). In this study we collected data from Korean National Health and Nutrition Examination Survey and built a decision tree after data pre-processing. The decision tree brings about five useful rules and their average classification accuracy is quite high (75.8%). In addition, the decision tree showed that high blood pressure and waist circumference are the most influential factors on the classification of the two groups. Our research results will serve as good guidelines for clinicians to provide better treatment for patients with MS, such that they do not develop diabetes.
Abstract
The objective of this study is to derive specific classification rules that could be used to prevent individuals with Metabolic Syndrome (MS) from developing diabetes. Specifically, we aim to identify rules which classify individuals with MS into those without diabetes (class 0) and those with diabetes (class 1). In this study we collected data from Korean National Health and Nutrition Examination Survey and built a decision tree after data pre-processing. The decision tree brings about five useful rules and their average classification accuracy is quite high (75.8%). In addition, the decision tree showed that high blood pressure and waist circumference are the most influential factors on the classification of the two groups. Our research results will serve as good guidelines for clinicians to provide better treatment for patients with MS, such that they do not develop diabetes.
참고문헌 (24)
- Scott M. Grundy, "Definition of Metabolic Syndrome", Circulation, 109(3), pp.433~438 (2004), Ovid Technologies (Wolters Kluwer Health)[DOI]
- Jing Chen, "The Metabolic Syndrome and Chronic Kidney Disease in U.S. Adults", Annals of Internal Medicine, 140(3), pp.167~ (2004), American College of Physicians[DOI]
- Masahide Hamaguchi, "The Metabolic Syndrome as a Predictor of Nonalcoholic Fatty Liver Disease", Annals of Internal Medicine, 143(10), pp.722~ (2005), American College of Physicians[DOI]
- N. Satar, "Metabolic syndrome with and without C-reactive protein as a predictor of coronary heart disease and diabetes in the west of Scotland coronary prevention study", ACC Current Journal Review, 12(6), pp.26~ (2003), Elsevier BV[DOI]
- K George MM Alberti, "The metabolic syndrome—a new worldwide definition", The Lancet, 366(9491), pp.1059~1062 (2005), Elsevier BV[DOI]
- Steven M. Haffner, "Mortality from Coronary Heart Disease in Subjects with Type 2 Diabetes and in Nondiabetic Subjects with and without Prior Myocardial Infarction", New England Journal of Medicine, 339(4), pp.229~234 (1998), Massachusetts Medical Society[DOI]
- R. Klein, "Hyperglycemie and Microvascular and Macrovascular Disease in Diabetes", Diabetes Care, 18(2), pp.258~268 (1995), American Diabetes Association[DOI]
- Seppo Lehto, "Predictors of Stroke in Middle-Aged Patients With Non–Insulin-Dependent Diabetes", Stroke, 27(1), pp.63~68 (1996), Ovid Technologies (Wolters Kluwer Health)[DOI]
- David R. Whiting, "IDF Diabetes Atlas: Global estimates of the prevalence of diabetes for 2011 and 2030", Diabetes Research and Clinical Practice, 94(3), pp.311~321 (2011), Elsevier BV[DOI]
- Naveed Sattar, "Can metabolic syndrome usefully predict cardiovascular disease and diabetes? Outcome data from two prospective studies", The Lancet, 371(9628), pp.1927~1935 (2008), Elsevier BV[DOI]
- David D. Waters, "Predictors of New-Onset Diabetes in Patients Treated With Atorvastatin", Journal of the American College of Cardiology, 57(14), pp.1535~1545 (2011), Elsevier BV[DOI]
- Kayo Kurotani, "Metabolic syndrome components and diabetes incidence according to the presence or absence of impaired fasting glucose: The Japan Epidemiology Collaboration on Occupational Health Study", Journal of Epidemiology, 27(9), pp.408~412 (2017), Japan Epidemiological Association[DOI]
- M. P. Stern, "Does the Metabolic Syndrome Improve Identification of Individuals at Risk of Type 2 Diabetes and", or Cardiovascular Disease?, 2004, pp.27(11), 2676~2681[DOI]
- 김한결, "국민건강영양조사를 활용한 대사증후군 유병 예측모형 개발을 위한 융복합 연구: 데이터마이닝을 활용하여", 디지털융복합연구, 14(2), pp.325~332 (2016), 한국디지털정책학회[DOI]
- Jae-Min Park, "Association between the triglyceride to high-density lipoprotein cholesterol ratio and insulin resistance in Korean adolescents: a nationwide population-based study", Journal of Pediatric Endocrinology and Metabolism, 0(0) (2016), Walter de Gruyter GmbH[DOI]
- 오정윤, "데이터 마이닝 기법을 활용한 스마트팩토리 도입 기업의 특성 분석", 한국융합학회논문지, 9(5), pp.179~189 (2018), 한국융합학회[DOI]
- 김주창, "스마트 공장에서 의사결정 모델을 이용한 순차 마이닝 기반 제조공정", 한국융합학회논문지, 9(3), pp.53~59 (2018), 한국융합학회[DOI]
- 구진희, "사물인터넷 환경에서 제품 불량 예측을 위한 기계 학습 모델에 관한 연구", 융합정보논문지, 7(1), pp.55~60 (2017), 중소기업융합학회[DOI]
- D. Lavanya, "Performance evaluation of decision tree classifiers on medical datasets", International Journal of Computer Applications, 26(4), pp.1~4 (2011)
- Nada Lavrač, "Selected techniques for data mining in medicine", Artificial Intelligence in Medicine, 16(1), pp.3~23 (1999), Elsevier BV[DOI]
- 김태호, "제2형 당뇨병환자에서 대사증후군의 유병률", Diabetes and Metabolism Journal, 33(1), pp.40~47 (2009), 대한당뇨병학회
- Z. S. Lee, "Plasma insulin, growth hormone, cortisol, and central obesity among young Chinese type 2 diabetic patients", Diabetes Care, 22(9), pp.1450~1457 (1999), American Diabetes Association[DOI]
- Tasnima Siddiquee, "Association of general and central obesity with diabetes and prediabetes in rural Bangladeshi population", Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 9(4), pp.247~251 (2015), Elsevier BV[DOI]
- G. M. Rao, "Serum glutamic oxaloacetic transaminase(GOT)and glutamic pyruvic transaminase(GPT)levels in diabetes mellitus", Indian Journal of Medical Sciences, 43(5), pp.118~121 (1989)