Journal of the Korea Academia-Industrial cooperation Society (한국산학기술학회논문지)

The Korea Academia-Industrial cooperation Society (한국산학기술학회)
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Factors influencing metabolic syndrome perception and exercising behaviors in Korean adults: Data mining approach

대사증후군의 인지와 신체활동 실천에 영향을 미치는 요인: 데이터 마이닝 접근

  • Lee, Soo-Kyoung (College of Nursing, The Research Institute of Nursing Science, Keimyung University) ;
  • Moon, Mikyung (College of Nursing, The Research Institute of Nursing Science, Kyungpook National University)
  • 이수경 (계명대학교 간호대학, 간호과학연구소) ;
  • 문미경 (경북대학교 간호대학, 간호과학연구소)
  • Received : 2017.11.06
  • Accepted : 2017.12.08
  • Published : 2017.12.31
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Abstract

This study was conducted to determine which factors would predict metabolic syndrome (MetS) perception and exercise by applying a machine learning classifier, or Extreme Gradient Boosting algorithm (XGBoost) from July 2014 to December 2015. Data were obtained from the Korean Community Health Survey (KCHS), representing different community-dwelling Korean adults 19 years and older, from 2009 to 2013. The dataset includes 370,430 adults. Outcomes were categorized as follows based on the perception of MetS and physical activity (PA): Stage 1 (no perception, no PA), Stage 2 (perception, no PA), and Stage 3 (perception, PA). Features common to all questionnaires for the last 5 years were selected for modeling. Overall, there were 161 features, categorical except for age and the visual analogue scale (EQ-VAS). We used the Extreme Boosting algorithm in R programming for a model to predict factors and achieved prediction accuracy in 0.735 submissions. The top 10 predictive factors in Stage 3 were: age, education level, attempt to control weight, EQ mobility, nutrition label checks, private health insurance, EQ-5D usual activities, anti-smoking advertising, EQ-VAS, education in health centers for diabetes, and dental care. In conclusion, the results showed that XGBoost can be used to identify factors influencing disease prevention and management using healthcare bigdata.

본 연구는 기계 학습법 중 하나인 XGBoost를 이용하여 대사증후군을 인지하고 신체활동을 수행하는 집단을 예측하고자 2014년 7월부터 2015년 12월까지 시도되었다. 이에 2009-2013년 지역사회건강조사를 연구자료로 사용하였고 370,430명의 성인을 분석에 포함하였다. 본 연구의 종속변수는 대사증후군의 인지 및 신체활동 실천정도에 따른 단계로 3단계로 구분하였다:Stage 1(무인지, 무 신체활동), Stage 2(인지, 무 신체활동), and Stage 3(인지, 신체활동). 예측변수로는 5년간의 지역사회건강조사 중 공통으로 수집된 문항으로부터 161개의 특성을 선택하였다. 자료 분석을 위해 R program을 이용하여 XGBoost 알고리즘을 적용하였다. 분석 결과 정확도는 0.735 이었으며, 가장 영향을 미치는 10개의 특성은 나이, 교육수준, 체중조절시도 경험, EQ-5D 운동능력, 영양표시 확인, 개인 건강보험가입 유무, EQ-5D 일상활동, 금연광고경험 여부, 통증유무, 당뇨에 대한 보건기관의 교육 경험 순으로 확인되었다. 본 연구결과는 XGBoost가 보건의료빅데이터를 이용한 질병의 예방과 관리에 영향을 주는 요인을 확인하는데 유용한 도구임을 보여주었다. 또한, 본 연구를 통해 대사증후군에 취약한 계층을 확인하고 이를 위한 교육프로그램 개발에 도움을 줄 수 있을 것으로 보인다.

Keywords

References

  1. S. M. Grundy, J. I. Cleeman, S. R. Daniels, K. A. Donato, R. H. Eckel, B. A. Franklin, D. J. Gordon, R. M. Krauss, P. J. Savage, S. C. Smith, J. A. Spertus, "Diagnosis and management of the metabolic syndrome", Circulation, vol. 112, no. 17, pp. 2735-52, 2005. DOI: https://doi.org/10.1161/CIRCULATIONAHA.105.169404
  2. S. Mottillo, K. B. Filion, J. Genest, L. Joseph, L. Pilote, P. Poirier, S. Rinfret, E. L. Schiffrin, M. L. Eisenberg, "The metabolic syndrome and cardiovascular risk: a systematic review and meta-analysis", Journal of the American College of Cardiology, vol. 56, no. 14, pp. 1113-32, Sep 28, 2010. DOI: https://doi.org/10.1016/j.jacc.2010.05.034
  3. J. O. Hill, D. Bessesen, "What to do about the metabolic syndrome?", Archives of internal medicine, vol. 163, no. 4, pp. 395-7, 2003. DOI: https://doi.org/10.1001/archinte.163.4.395
  4. M. F. Jumean, Y. Korenfeld, V. K. Somers, K. S. Vickers, R. J. Thomas, F. Lopez-Jimenez, "Impact of diagnosing metabolic syndrome on risk perception", American journal of health behavior, vol. 36, no. 4, pp. 522-3, Jul 1, 2012. DOI: https://doi.org/10.5993/AJHB.36.4.9
  5. K. Glanz, B. Rimer, K. Viswanath, eds. Health behavior and health education: theory, research, and practice. John Wiley & Sons, 2008.
  6. J. A. Lee, J. S. Lee, J. H. Park, "Metabolic syndrome perception and exercise behaviors in the elderly", Korean Journal of Health Education and Promotion, vol. 29, No. 5. pp. 61-75, 2012.
  7. K. J. Stewart, A. C. Bacher, K. Turner, J. G. Lim, P. S. Hees, E. P. Shapiro, M. Tayback, P. Ouyang, "Exercise and risk factors associated with metabolic syndrome in older adults." American journal of preventive medicine, vol. 28, no. 1 pp. 9-18, Jan, 2005. DOI: https://doi.org/10.1016/j.amepre.2004.09.006
  8. J. R. Churilla, E. C. Fitzhugh, "Relationship between leisure-time physical activity and metabolic syndrome using varying definitions: 1999-2004 NHANES", Diabetes and Vascular Disease Research, vol. 6, no. 2 pp. 100-9, Apr, 2009. DOI: https://doi.org/10.1177/1479164109336040
  9. S. K. Park, J. L. Larson, "The relationship between physical activity and metabolic syndrome in people with chronic obstructive pulmonary disease", The Journal of cardiovascular nursing, vol. 29, no. 6, pp. 499, Nov, 2014. DOI: https://doi.org/10.1097/JCN.0000000000000096
  10. A. Bener, M. T. Yousafzai, S. Darwish, A. O. Al-Hamaq, E. A. Nasralla, M. Abdul-Ghani, "2013. Obesity index that better predict metabolic syndrome: body mass index, waist circumference, waist hip ratio, or waist height ratio", Journal of obesity, 2013. DOI: https://doi.org/10.1155/2013/269038
  11. A. Scuteri, S. Laurent, F. Cucca, J. Cockcroft, P. G. Cunha, L. R. Manas, F. U. M. Raso, M. L. Muiesan, L. Ryliskyte, E. Rietzschel, J. Strait, "Metabolic syndrome across Europe: different clusters of risk factors", European journal of preventive cardiology, vol. 22, no. 4, pp. 486-491, 2015. DOI: https://doi.org/10.1177/2047487314525529
  12. Y. T. Kim, B. Y. Choi, K.O. Lee, H. Kim, J. H. Chun, S. Y. Kim, D. H. Lee, T. A. Ghim, D. S. Lim, Y. W. Kang, T. Y Lee, "Overview of Korean Community Health Survey", Journal of the Korean Medical Association/Taehan Uisa Hyophoe Chi, vol. 55, no. 1, pp. 74-83, 2012. https://doi.org/10.5124/jkma.2012.55.1.74
  13. K. H. Choi, J. Heo, S. Kim, Y. J. Jeon, M. Oh, "Factors associated with breast and cervical cancer screening in Korea: data from a national community health survey", Asia Pacific Journal of Public Health, vol. 25, no. 6, pp. 476-86, 2013. DOI: https://doi.org/10.1177/1010539513506601
  14. W. Raghupathi, V. Raghupath, "Big data analytics in healthcare: promise and potential.", Health information science and systems, vol. 2, no. 1, pp. 3, 2014. https://doi.org/10.1186/2047-2501-2-3
  15. R. Bellazzi, B. Zupan, "Predictive data mining in clinical medicine: current issues and guidelines", International journal of medical informatics, vol. 77, no. 2 pp. 81-97, Feb 29, 2008. DOI: https://doi.org/10.1016/j.ijmedinf.2006.11.006
  16. T. Chen, C. Guestrin, "Xgboost: A scalable tree boosting system.", In Proceedings of the 22nd ACM sigkdd international conference on knowledge discovery and data mining, pp. 785-794. ACM, 2016. DOI: https://doi.org/10.1145/2939672.2939785
  17. I. Babajide Mustapha, F. Saeed, "Bioactive molecule prediction using extreme gradient boosting", Molecules, vol. 21, no. 8, pp. 983, Jul 28, 2016. DOI: https://doi.org/10.3390/molecules21080983
  18. L. Torlay, M. Perrone-Bertolotti, E. Thomas, M. Baciu, "Machine learning-XGBoost analysis of language networks to classify patients with epilepsy", Brain Informatics, vol. 22, no. 1, April, 2017.
  19. A. M. Arymurthy, "Predicting the status of water pumps using data mining approach. InBig Data and Information Security (IWBIS)", International Workshop, IEEE, pp. 57-64, Oct, 2016. DOI: https://doi.org/10.1109/IWBIS.2016.7872890
  20. P. Ajit, "Prediction of Employee Turnover in Organizations using Machine Learning Algorithms", algorithms, vol. 4, no. 5, pp. C5, 2016
  21. M. Sokolova, G. Lapalme, "A systematic analysis of performance measures for classification tasks.", Information Processing & Management, vol. 31, no. 4, pp. 427-37, July, 2009. DOI: https://doi.org/10.1016/j.ipm.2009.03.002
  22. B. T. Tran, B. Y. Jeong, J. K. Oh, "The prevalence trend of metabolic syndrome and its components and risk factors in Korean adults: results from the Korean National Health and Nutrition Examination Survey 2008-2013", BMC Public Health, vol. 1, no. 17, pp. 1-8, 2017. DOI: https://doi.org/10.1186/s12889-016-3936-6
  23. R. Brooks, E. Group, "EuroQol: the current state of play", Health policy, vol. 37, no. 1, pp53-72, July, 1996. DOI: https://doi.org/10.1016/0168-8510(96)00822-6
  24. S. S. Park, Y. S. Yoon, S. W. Oh, "Health-related quality of life in metabolic syndrome: The Korea National Health and Nutrition Examination Survey 2005", diabetes research and clinical practice, vol. 91, no. 3, pp. 381-8, Mar 31, 2011. https://doi.org/10.1016/j.diabres.2010.11.010
  25. E. S. Ford, C. Li, "Metabolic syndrome and health-related quality of life among US adults", Annals of epidemiology, vol. 18, no. 3 pp. 165-71, Mar 31, 2008. DOI: https://doi.org/10.1016/j.annepidem.2007.10.009