디지털융복합연구 (Journal of Digital Convergence)

  • 제19권12호
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  • Pages.477-486
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  • 2021
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  • 2713-6434(pISSN)
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  • 2713-6442(eISSN)
한국디지털정책학회 (The Society of Digital Policy and Management)
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성별에 따른 대사증후군의 위험요인 탐색을 위한 융복합 연구

Convergence study to detect metabolic syndrome risk factors by gender difference

  • 이소은 (고려대학교 대학원 보건과학과) ;
  • 이현실 (고려대학교 보건과학대학 보건정책관리학부)
  • Lee, So-Eun (Department of Health Sciences, Graduate School, Korea University) ;
  • Rhee, Hyun-Sill (School of Health Policy and Management, College of Health Science, Korea University)
  • 투고 : 2021.09.24
  • 심사 : 2021.12.20
  • 발행 : 2021.12.28
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초록

본 연구의 목적은 국민건강영양조사 2016-2019년 자료 중 성인을 대상으로 대사증후군의 위험요인 탐색하고, 성별에 따른 위험요인의 차이를 규명하여 대사증후군 예방 및 치료에 기초자료로 제공하기 위함이다. 다양한 선행연구를 통해 대사증후군 위험요인을 수집하고, 4개의 머신러닝(Logistic Regression, Decision Tree, Naïve Bayes, Random Forest)의 방법을 이용하여 분석하였다. 남성과 여성 모두에서 Random Forest의 대사증후군 예측 정확도가 높았다. 대사증후군 유병에 영향을 주는 상위 위험요인으로는 여성과 남성 모두에서 BMI, 식이(지방, 비타민 C, 비타민 A, 단백질, 에너지 섭취), 기저질환의 개수, 연령으로 나타났다. 여성의 경우 교육수준과 초경 연령, 폐경 여부가 추가적으로 주요 위험요인으로 나타났고, 남성에 비해 연령과 기저질환의 개수에서 영향력이 큰 것으로 나타났다. 대사증후군을 예방하기 위해선 BMI, 식이, 질환의 이환, 초경 및 폐경여부를 고려하여 접근해야하며 후속 연구를 통해 다양한 중재 전략을 수립하고 검증해야 할 것이다.

This study was conducted to detect metabolic syndrome risk factors and gender difference in adults. 18,616 cases of adults are collected by Korea Health and Nutrition Examination Study from 2016 to 2019. Using 4 types of machine Learning(Logistic Regression, Decision Tree, Naïve Bayes, Random Forest) to predict Metabolic Syndrome. The results showed that the Random Forest was superior to other methods in men and women. In both of participants, BMI, diet(fat, vitamin C, vitamin A, protein, energy intake), number of underlying chronic disease and age were the upper importance. In women, education level, menarche age, menopause was additional upper importance and age, number of underlying chronic disease were more powerful importance than men. Future study have to verify various strategy to prevent metabolic syndrome.

키워드

참고문헌

  1. National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) (2002). Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report. Circulation, 106(25), 3143-3421. DOI : 10.1161/circ.106.25.3143.
  2. Grundy, S. M., Cleeman, J. I., Daniels, S. R., Donato, K. A., Eckel, R. H., Franklin, B. A., ... & Costa, F. (2005). Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute scientific statement. Circulation, 112(17), 2735-2752. DOI : 10.1161/CIRCULATIONAHA.105.169404
  3. Grundy, S. M. (2007). Metabolic syndrome: a multiplex cardiovascular risk factor. The Journal of Clinical Endocrinology & Metabolism, 92(2), 399-404. DOI : 10.1210/jc.2006-0513
  4. Huh, J. H., Kang, D. R., Kim, J. Y., & Koh, K. K. (2021). Metabolic Syndrome Fact Sheet 2021: Executive Report. CardioMetabolic Syndrome Journal, 1. DOI : 10.51789/cmsj.2021.1.e15
  5. Shin, S., & Lee, S. (2019). Association between total diet quality and metabolic syndrome incidence risk in a prospective cohort of Korean adults. Clinical nutrition research, 8(1), 46-54. DOI : 10.7762/cnr.2019.8.1.46
  6. Grundy, S. M. (2008). Metabolic syndrome pandemic. Arteriosclerosis, thrombosis, and vascular biology, 28(4), 629-636. DOI : 10.1161/ATVBAHA.107.151092
  7. Bang, S. Y. (2019). The relations between metabolic syndrome, physical activity, and dietary patterns in Korean adults. Journal of the Korea Academia-Industrial cooperation Society, 20(2), 662-672. DOI : 10.5762/KAIS.2019.20.2.662
  8. Han, M. (2011). Metabolic syndrome emerging from menopause. The Journal of Korean Society of Menopause, 17(3), 127-135. DOI : 10.6118/jksm.2011.17.3.127
  9. Won JC, Hong JW, Noh JH, Kim DJ(2016) association between age at menarche and risk factors for cardiovascular diseases in Korean women. The 2010 to 2013 Korea National Health and Nutrition Examination Survey. Med (Baltimore) 95(18), 3580-3589. DOI : 10.1097/MD.0000000000003580
  10. Kang, H. M., & Kim, D. J. (2012). Gender differences in the association of socioeconomic status with metabolic syndrome in middle-aged Koreans. Korean Journal of Medicine, 82(5), 569-575. DOI:10.3904/kjm.2012.82.5.569
  11. Kim, E., & Oh, S. W. (2012). Gender differences in the association of occupation with metabolic syndrome in Korean adults. The Korean Journal of Obesity, 21(2), 108-114. DOI : 10.7570/kjo.2012.21.2.108
  12. Seo, J. M., Lim, N. K., Lim, J. Y., & Park, H. Y. (2016). Gender difference in association with socioeconomic status and incidence of metabolic syndrome in Korean adults. The Korean Journal of Obesity, 25(4), 247-254. DOI : 10.7570/kjo.2016.25.4.247
  13. Oh, S. I., Hwang, Y. S., & Rhyu, M. J. (2013). Effects of a Combined Exercise Program on the Body Composition, Health-related Physical Fitness, and Metabolic Syndrome Risk Factor in Middle-aged Women. The Official Journal of the Korean Academy of Kinesiology, 15(3), 91-100. DOI : 10.15758/jkak.2013.15.3.91
  14. Kim, A. (2018). Effect of health behaviors, dietary habits, and psychological health on metabolic syndrome in one-person households among Korean young adults. Journal of Digital Convergence, 16(7), 493-509. DOI : 10.14400/JDC.2018.16.7.493
  15. Lim, M., & Kim, J. (2020). Association between fruit and vegetable consumption and risk of metabolic syndrome determined using the Korean Genome and Epidemiology Study (KoGES). European journal of nutrition, 59(4), 1667-1678. DOI : 10.1007/s00394-019-02021-5
  16. Yang, H., Kim, H., Kim, J. M., Chung, H. W., & Chang, N. (2016). Associations of dietary intake and metabolic syndrome risk parameters in Vietnamese female marriage immigrants in South Korea: The KoGES follow-up study. Nutrition research and practice, 10(3), 313-320. DOI : 10.4162/nrp.2016.10.3.313
  17. Kim, D. I., Kim, J. Y., Lee, M. K., Lee, H. D., Lee, J. W., & Jeon, J. Y. (2012). The relationship between fitness, BMI and risk factors of metabolic syndrome among university students in Korea. The Korean Journal of Obesity, 21(2), 99-107. DOI : 10.7570/kjo.2012.21.2.99
  18. Kim, J., Yoon, D. W., Lee, S. K., Lee, S., Choi, K. M., Robert, T. J., & Shin, C. (2017). Concurrent presence of inflammation and obstructive sleep apnea exacerbates the risk of metabolic syndrome: a KoGES 6-year follow-up study. Medicine, 96(7). DOI : 10.1097/MD.0000000000004488
  19. Cho, J., Yoon, E., & Park, S. H. (2019). Association of relative handgrip strength with the incidence of metabolic syndrome in korean adults: a community based cohort study. Exercise Science, 28(3), 303-310. DOI : 10.15857/ksep.2019.28.3.303
  20. Choe, E. K., Rhee, H., Lee, S., Shin, E., Oh, S. W., Lee, J. E., & Choi, S. H. (2018). Metabolic syndrome prediction using machine learning models with genetic and clinical information from a nonobese healthy population. Genomics & informatics, 16(4). DOI : 10.5808/GI.2018.16.4.e31
  21. Korea Centers for Disease Control and Prevention. (2020). Guideline for raw data use of The Seventh Korea National Health and Nutrition Examination Survey (KNHANES VII), 2016-2018.
  22. Expert Panel on Detection, E. (2001). Executive summary of the third report of the National Cholesterol Education Program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (adult treatment panel III). Jama, 285(19), 2486-2497. DOI : 10.1001/jama.285.19.2486
  23. Yoon, Y. S., & Oh, S. W. (2014). Optimal waist circumference cutoff values for the diagnosis of abdominal obesity in Korean adults. Endocrinology and Metabolism, 29(4), 418-426. DOI : 10.3803/EnM.2014.29.4.418
  24. Yang, Y. H., Kim, J. S., & Jeong, S. H. (2020). Prediction of dental caries in 12-year-old children using machine-learning algorithms. Journal of Korean Academy of Oral Health, 44(1), 55-63. DOI : 10.11149/jkaoh.2020.44.1.55
  25. Seul, M. S. (2016). Current Status and Future Developments of Machine Learning Artificial Intelligence in Law Focusing the Cusp of Machine Learning in US and Discourses over Legal Profession and Law School Education. The Justice, 156, 269-302.
  26. Cho, S. Y., Kim, S. H., Kang, S. H., Lee, K. J., Choi, D., Kang, S., ... & Chae, I. H. (2021). Pre-existing and machine learning-based models for cardiovascular risk prediction. Scientific reports, 11(1), 1-10. DOI : 10.1038/s41598-021-88257-w
  27. Lee, B. J. (2019). Prediction model of hypercholesterolemia using body fat mass based on machine learning. The Journal of the Convergence on Culture Technology, 5(4), 413-420. DOI : 10.17703/JCCT.2019.5.4.413
  28. Park, J. H., Cho, H. E., Kim, J. H., Wall, M. M., Stern, Y., Lim, H., ... & Cha, J. (2020). Machine learning prediction of incidence of Alzheimer's disease using large-scale administrative health data. NPJ digital medicine, 3(1), 1-7. DOI : 10.1038/s41746-020-0256-0
  29. Kim, H.-S. (2019). Convergence Analysis of Risk factors for Readmission in Cardiovascular Disease: A Machine Learning Approach. Journal of Convergence for Information Technology, 9(12), 115-123. DOI : 10.22156/CS4SMB.2019.9.12.115
  30. In-Ja, L., & Junho, L. (2020). Predictive of Osteoporosis by Tree-based Machine Learning Model in Post-menopause Woman. Journal of Radiological Science and Technology, 43(6), 495-502. DOI : 10.17946/JRST.2020.43.6.495
  31. Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society: Series B (Methodological), 20(2), 215-232. DOI : 10.1111/j.2517-6161.1958.tb00292.x
  32. Lee, S. M., Park, K. D., & Kim, I. K. (2020). Comparison of machine learning algorithms for Chl-a prediction in the middle of Nakdong river (focusing on water quality and quantity factors). Journal of Korean Society of Water and Wastewater, 34(4), 277-288. DOI : 10.11001/jksww.2020.34.4.277
  33. Jeong, M. C., Lee, J. H., & Oh, H. Y. (2020). Ensemble Machine Learning Model Based Youtube Spam Comment Detection. Journal of the Korea Institute of Information and Communication Engineering, 24(5), 576-583. DOI : 10.3745/KTSDE.2021.10.7.257
  34. Lim, J. S., & Kim, J. M. (2014). An empirical comparison of machine learning models for classifying emotions in Korean Twitter. Journal of Korea Multimedia Society, 17(2), 232-239. DOI : 10.9717/kmms.2014.17.2.232
  35. Go, W. S., Yoon, C. G., Rhee, H. P., Hwang, S. J., & Lee, S. W. (2019). A Study on the prediction of BMI (Benthic Macroinvertebrate Index) using Machine Learning Based CFS (Correlation-based Feature Selection) and Random Forest Model. Journal of Korean Society on Water Environment, 35(5), 425-431. DOI : 10.15681/KSWE.2019.35.5.425
  36. Kim, S. J., & Ahn, H. (2016). Application of random forests to corporate credit rating prediction. The Journal of Business and Economics, 32(1), 187-211. DOI : 10.22793/indinn.2016.32.1.006
  37. Jung, H., & Kim, J. W. (2017). A machine learning approach for mechanical motor fault diagnosis. Journal of the Society of Korea Industrial and Systems Engineering, 40(1), 57-64. DOI : 10.11627/jkise.2017.40.1.057
  38. Yi, D. W., Khang, A. R., Lee, H. W., Son, S. M., & Kang, Y. H. (2018). Relative handgrip strength as a marker of metabolic syndrome: the Korea National Health and Nutrition Examination Survey (KNHANES) VI (2014-2015). Diabetes, metabolic syndrome and obesity : targets and therapy, 11, 227-240. DOI : 10.2147/DMSO.S166875
  39. Kim, M., & Sohn, C. (2016). Analysis of dietary inflammatory index of metabolic syndrome in Korean: data from the health examinee cohort (2012-2014). Korean J Hum Ecol, 25, 823-834. DOI : 10.5934/kjhe.2016.25.6.823
  40. Oh, G. C., Kang, K. S., Park, C. S., Sung, H. K., Ha, K. H., Kim, H. C., ... & Lee, H. Y. (2018). Metabolic syndrome, not menopause, is a risk factor for hypertension in peri-menopausal women. Clinical hypertension, 24(1), 1-8. DOI : 10.1186/s40885-018-0099-z
  41. Chang, C. J., Lai, M. M., Lin, C. C., Liu, C. S., Li, T. C., Li, C. I., & Lin, W. Y. (2016). Age at menarche and its association with the metabolic syndrome in Taiwan. Obesity research & clinical practice, 10 Suppl 1, S26-S34. DOI : 10.1016/j.orcp.2015.10.003
  42. Jeong E. J. & Jung B. M. (2020). Analysis of Anthropometric and Behavioral Factors of Korean Female Adolescents According to Age of Menarche: 2013~2017 Korea National Health and Nutrition Examination Survey. The Korean Journal of Community Living Science, 31(3), 393-409. DOI : 10.7856/kjcls.2020.31.3.393