DOI QR코드

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Deriving rules for identifying diabetic among individuals with metabolic syndrome

대사증후군 환자 가운데 당뇨환자를 찾기 위한 규칙 도출

  • Received : 2018.08.16
  • Accepted : 2018.11.20
  • Published : 2018.11.28

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.

Keywords

Data mining;Decision tree;Diabetes;Metabolic syndrome;KHANES

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Fig. 1. The decision tree built from the training dataset. The bar at the bottom of the figure indicates the proportion of each class in the leaf node.

Table 1. Details of the features remained after pre-processing.

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Table 2. The classification results of the decision tree

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Table 3. 14 rules generated from the decision tree.

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Table 4. Comparison of the four algorithms

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Acknowledgement

Supported by : Korea University Business School

References

  1. S. M. Grundy, H. B. Brewer, J. I. Cleeman, S. C. Smith & C. Lenfant. (2004). Definition of metabolic syndrome. Circulation, 109(3), 433-438. DOI : 10.1161/01.CIR.0000111245.75752.C6 https://doi.org/10.1161/01.CIR.0000111245.75752.C6
  2. J. Chen et al. (2004). The metabolic syndrome and chronic kidney disease in us adults. Annals of Internal Medicine, 140(3), 167-174. DOI : 10.7326/0003-4819-140-3-200402030-00007 https://doi.org/10.7326/0003-4819-140-3-200402030-00007
  3. M. Hamaguchi et al. (2005). The metabolic syndrome as a predictor of nonalcoholic fatty liver disease. Annals of Internal Medicine, 143(10), 722-728. DOI : 10.7326/0003-4819-143-10-200511150-00009 https://doi.org/10.7326/0003-4819-143-10-200511150-00009
  4. N. Sattar et al. (2003). 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. Circulation, 108(4), 414-419. DOI : 10.1016/j.accreview.2003.09.016 https://doi.org/10.1161/01.CIR.0000080897.52664.94
  5. K. G. M. Alberti, P. Zimmet & J. Shaw. (2005). The metabolic syndrome-a new worldwide definition. The Lancet, 366(9491), 1059-1062. DOI : 10.1016/S0140-6736(05)67402-8 https://doi.org/10.1016/S0140-6736(05)67402-8
  6. S. M. Haffner, S. Lehto, T. Ronnemaa, K. Pyorala & M. Laakso. (1998). 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), 229-234. DOI : 10.1056/NEJM199807233390404 https://doi.org/10.1056/NEJM199807233390404
  7. R. Klein. (1995). Hyperglycemia and microvascular and macrovascular disease in diabetes. Diabetes Care, 18(2), 258-268. DOI : 10.2337/diacare.18.2.258 https://doi.org/10.2337/diacare.18.2.258
  8. S. Lehto, T. Rönnemaa, K. Pyorala & M. Laakso. (1996). Predictors of stroke in middle-aged patients with non- insulin-dependent diabetes. Stroke, 27(1), 63-68. DOI : 10.1161/01.STR.27.1.63 https://doi.org/10.1161/01.STR.27.1.63
  9. D. R. Whiting, L. Guariguata, C. Weil & J. Shaw. (2011). Idf diabetes atlas: global estimates of the prevalence of diabetes for 2011 and 2030. Diabetes Research and Clinical Practice, 94(3), 311-321. DOI : 10.1016/j.diabres.2011.10.029 https://doi.org/10.1016/j.diabres.2011.10.029
  10. N. Sattar et al. (2008). Can metabolic syndrome usefully predict cardiovascular disease and diabetes? outcome data from two prospective studies. The Lancet, 371(9628), 1927-1935. DOI :10.1016/S0140-6736(08)60602-9 https://doi.org/10.1016/S0140-6736(08)60602-9
  11. D. D. Waters et al. (2011). Predictors of new-onset diabetes in patients treated with atorvastatin: results from 3 large randomized clinical trials. Journal of the American College of Cardiology, 57(14), 1535-1545.  DOI : 10.1016/j.jacc.2010.10.047 https://doi.org/10.1016/j.jacc.2010.10.047
  12. K. Kurotani et al. (2017). 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), 408-412. DOI : 10.1016/j.je.2016.08.015 https://doi.org/10.1016/j.je.2016.08.015
  13. M. P. Stern, K. Williams, C. González-Villalpando, K. J. Hunt & S. M. Haffner. (2004). Does the metabolic syndrome improve identification of individuals at risk of type 2 diabetes and/or cardiovascular disease? Diabetes Care, 27(11), 2676-2681. DOI : 10.2337/diacare.27.11.2676 https://doi.org/10.2337/diacare.27.11.2676
  14. H. K. Kim, K. H. Choi, S. W. Lim & H. S. Rhee. (2016). Development of prediction model for prevalence of metabolic syndrome using data mining : korea national health and nutrition examination study. Journal of Digital Convergence, 14(2), 325-332. DOI : 10.14400/JDC.2016.14.2.325 https://doi.org/10.14400/JDC.2016.14.2.325
  15. J. M. Park, J. Y. Lee, J. J. Dong, D. C. Lee & Y. J. Lee. (2016). 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, 29(11), 1259-1265. DOI :10.1515/jpem-2016-0244 https://doi.org/10.1515/jpem-2016-0244
  16. J. Y. Oh & S. H. Choi. (2018). An analysis of the characteristics of companies introducing smart factory system using data mining technique. Journal of the Korea Convergence Society, 9(5), 179-189. DOI :10.15207/JKCS.2018.9.5.179 https://doi.org/10.15207/JKCS.2018.9.5.179
  17. J. C. Kim, H. I. Jung, H. Yoo & K. Y. Chung. (2018). Sequence mining based manufacturing process using decision model in cognitive factory. Journal of the Korea Convergence Society, 9(3), 53-59. DOI :10.15207/JKCS.2018.9.3.05 https://doi.org/10.15207/JKCS.2018.9.3.053
  18. J. H. Ku. (2017). A study of the machine learning model for product faulty prediction in internet of things environment. Journal of Convergence for Information Technology, 7(1), 55-60. DOI :10.22156/CS4SMB.2017.7.1.055 https://doi.org/10.22156/CS4SMB.2017.7.1.055
  19. D. Lavanya & K. U. Rani. (2011). Performance evaluation of decision tree classifiers on medical datasets. International Journal of Computer Applications, 26(4), 1-4. https://doi.org/10.5120/3095-4247
  20. N. Lavrac. (1999). Selected Techniques for data mining in medicine. Artificial Intelligence in Medicine, 16(1), 3-23. DOI : 10.1016/S0933-3657(98)00062-1 https://doi.org/10.1016/S0933-3657(98)00062-1
  21. T. H. Kim et al. (2009). Prevalence of the metabolic syndrome in type 2 diabetic patients. Korean Diabetes Journal, 33(1), 40-47. DOI : 10.4093/kdj.2009.33.1.40 https://doi.org/10.4093/kdj.2009.33.1.40
  22. Z. Lee et al. (1999). Plasma insulin, growth hormone, cortisol, and central obesity among young chinese type 2 diabetic patients. Diabetes Care, 22(9), 1450-1457. DOI : 10.2337/diacare.22.9.1450 https://doi.org/10.2337/diacare.22.9.1450
  23. T. Siddiquee et al. (2015). Association of general and central obesity with diabetes and prediabetes in rural bangladeshi population. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 9(4), 247-251. DOI : 10.1016/j.dsx.2015.02.002 https://doi.org/10.1016/j.dsx.2015.02.002
  24. G. M. Rao, L. O. Morghom, M. N. Kabur, B. M. B. Mohmud & K. Ashibani. (1989). Serum glutamic oxaloacetic transaminase (GOT) and glutamic pyruvic transaminase (GPT) levels in diabetes mellitus. Indian Journal of Medical Sciences, 43(5), 118-121.