DOI QR코드

DOI QR Code

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

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.

Acknowledgement

Supported by : Keimyung University

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 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 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 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 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 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 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 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 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 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 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 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 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 https://doi.org/10.1016/j.annepidem.2007.10.009