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Statistical Analysis for Risk Factors and Prediction of Hypertension based on Health Behavior Information

건강행위정보기반 고혈압 위험인자 및 예측을 위한 통계분석

  • Heo, Byeong Mun (Database/Bioinformatics Lab, School of Electrical & Computer Engineering, Chungbuk National University) ;
  • Kim, Sang Yeob (Database/Bioinformatics Lab, School of Electrical & Computer Engineering, Chungbuk National University) ;
  • Ryu, Keun Ho (Database/Bioinformatics Lab, School of Electrical & Computer Engineering, Chungbuk National University)
  • 허병문 (충북대학교 데이터베이스/바이오인포매틱스 연구실) ;
  • 김상엽 (충북대학교 데이터베이스/바이오인포매틱스 연구실) ;
  • 류근호 (충북대학교 데이터베이스/바이오인포매틱스 연구실)
  • Received : 2018.03.30
  • Accepted : 2018.04.27
  • Published : 2018.04.30

Abstract

The purpose of this study is to develop a prediction model of hypertension in middle-aged adults using Statistical analysis. Statistical analysis and prediction models were developed using the National Health and Nutrition Survey (2013-2016).Binary logistic regression analysis showed statistically significant risk factors for hypertension, and a predictive model was developed using logistic regression and the Naive Bayes algorithm using Wrapper approach technique. In the statistical analysis, WHtR(p<0.0001, OR = 2.0242) in men and AGE (p<0.0001, OR = 3.9185) in women were the most related factors to hypertension. In the performance evaluation of the prediction model, the logistic regression model showed the best predictive power in men (AUC = 0.782) and women (AUC = 0.858). Our findings provide important information for developing large-scale screening tools for hypertension and can be used as the basis for hypertension research.

본 연구는 통계분석을 이용한 중년 성인의 고혈압 예측모델 개발이 목적이다. 국민건강영양조사자료(2013년-2016년)를 사용하여 통계분석과 예측모델을 개발하였다. 이진 로지스틱 회귀분석으로 통계적 유의한 고혈압 위험인자를 제시하였으며, Wrapper 변수선택기법을 적용한 로지스틱회귀와 나이브베이즈 알고리즘을 이용하여 예측모델을 개발하였다. 통계분석에서 고혈압에 가장 높은 연관성을 갖는 인자는 남성에서 WHtR (p<0.0001, OR = 2.0242), 여성에서 AGE(p<0.0001, OR = 3.9185)로 나타났다. 예측모델의 성능평가에서, 로지스틱 회귀 모델이 남성(AUC = 0.782)과 여성(AUC = 0.858)에서 가장 좋은 예측력을 보였다. 우리의 연구 결과는 고혈압에 대한 대규모 스크리링 도구를 개발하는데 중요한 정보를 제공하며, 고혈압 연구에 대한 기반정보로 활용할 수 있다.

Keywords

Acknowledgement

Supported by : 정보통신기술진흥센터, 한국산업기술진흥원(KIAT)

References

  1. P. A. James et al., "2014 evidence-based guideline for the management of high blood pressure in adults: report from the panel members appointed to the Eighth Joint National Committee (JNC 8)," Jama, vol. 311, no. 5, pp. 507-520, 2014. https://doi.org/10.1001/jama.2013.284427
  2. C. M. Lawes, S. Vander Hoorn, and A. Rodgers, "Global burden of blood-pressure-related disease, 2001," The Lancet, vol. 371, no. 9623, pp. 1513-1518, 2008. https://doi.org/10.1016/S0140-6736(08)60655-8
  3. P. K. Whelton et al., "2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA Guideline for the Prevention, Detection, Evaluation, and Management of High Blood Pressure in Adults: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines," Journal of the American College of Cardiology, 2017.
  4. Q. Chan, J. Stamler, L. M. O. Griep, M. L. Daviglus, L. Van Horn, and P. Elliott, "An update on nutrients and blood pressure," Journal of atherosclerosis and thrombosis, p. 30000, 2016.
  5. J.-C. Tsai, W.-Y. Chang, C.-C. Kao, M.-S. Lu, Y.-J. Chen, and P. Chan, "Beneficial effect on blood pressure and lipid profile by programmed exercise training in Taiwanese patients with mild hypertension," Clinical and experimental hypertension, vol. 24, no. 4, pp. 315-324, 2002. https://doi.org/10.1081/CEH-120004234
  6. L. S. Pescatello, B. A. Franklin, R. Fagard, W. B. Farquhar, G. A. Kelley, and C. A. Ray, "Exercise and hypertension," Medicine & Science in Sports & Exercise, vol. 36, no. 3, pp. 533-553, 2004. https://doi.org/10.1249/01.MSS.0000115224.88514.3A
  7. N. S. Beckett et al., "Treatment of hypertension in patients 80 years of age or older," New England Journal of Medicine, vol. 358, no. 18, pp. 1887-1898, 2008. https://doi.org/10.1056/NEJMoa0801369
  8. J. A. Staessen, J.-G. Wang, and L. Thijs, "Cardiovascular protection and blood pressure reduction: a meta-analysis," The Lancet, vol. 358, no. 9290, pp. 1305-1315, 2001. https://doi.org/10.1016/S0140-6736(01)06411-X
  9. L. Grievink, J. Alberts, J. O'niel, and I. Gerstenbluth, "Waist circumference as a measurement of obesity in the Netherlands Antilles; associations with hypertension and diabetes mellitus," European journal of clinical nutrition, vol. 58, no. 8, pp. 1159-1165, 2004. https://doi.org/10.1038/sj.ejcn.1601944
  10. D. B. Carba, I. N. Bas, S. A. Gultiano, N. R. Lee, and L. S. Adair, "Waist circumference and the risk of hypertension and prediabetes among Filipino women," European journal of nutrition, vol. 52, no. 2, pp. 825-832, 2013. https://doi.org/10.1007/s00394-012-0390-9
  11. G. T. Ko, J. C. Chan, C. S. Cockram, and J. Woo, "Prediction of hypertension, diabetes, dyslipidaemia or albuminuria using simple anthropometric indexes in Hong Kong Chinese," International Journal of Obesity & Related Metabolic Disorders, vol. 23, no. 11, 1999.
  12. M. Dalton et al., "Waist circumference, waist-hip ratio and body mass index and their correlation with cardiovascular disease risk factors in Australian adults," Journal of internal medicine, vol. 254, no. 6, pp. 555-563, 2003. https://doi.org/10.1111/j.1365-2796.2003.01229.x
  13. W.-C. Li, I.-C. Chen, Y.-C. Chang, S.-S. Loke, S.-H. Wang, and K.-Y. Hsiao, "Waist-to-height ratio, waist circumference, and body mass index as indices of cardiometabolic risk among 36,642 Taiwanese adults," European journal of nutrition, vol. 52, no. 1, pp. 57-65, 2013. https://doi.org/10.1007/s00394-011-0286-0
  14. A. Colin Bell, L. S. Adair, and B. M. Popkin, "Ethnic Differences in the Association between Body Mass Index and Hypertension," American Journal of Epidemiology, vol. 155, no. 4, pp. 346-353, 2002. https://doi.org/10.1093/aje/155.4.346
  15. M. Emamian et al., "Association of hematocrit with blood pressure and hypertension," Journal of clinical laboratory analysis, 2017.
  16. D. E. Singer, D. M. Nathan, K. M. Anderson, P. W. Wilson, and J. C. Evans, "Association of HbA1c with prevalent cardiovascular disease in the original cohort of the Framingham Heart Study," Diabetes, vol. 41, no. 2, pp. 202-208, 1992. https://doi.org/10.2337/diab.41.2.202
  17. M. G. Marmot et al., "Alcohol and blood pressure: the INTERSALT study," Bmj, vol. 308, no. 6939, pp. 1263-1267, 1994. https://doi.org/10.1136/bmj.308.6939.1263
  18. C. f. D. Control and Prevention, "Vital signs: awareness and treatment of uncontrolled hypertension among adults--United States, 2003-2010," MMWR. Morbidity and mortality weekly report, vol. 61, p. 703, 2012.
  19. I. Hajjar and T. A. Kotchen, "Trends in prevalence, awareness, treatment, and control of hypertension in the United States, 1988-2000," Jama, vol. 290, no. 2, pp. 199-206, 2003. https://doi.org/10.1001/jama.290.2.199
  20. R. Kohavi and G. H. John, "Wrappers for feature subset selection," Artificial intelligence, vol. 97, no. 1-2, pp. 273-324, 1997. https://doi.org/10.1016/S0004-3702(97)00043-X
  21. J. Halloran, "Classification: Naive bayes vs logistic regression", pp.1-24, 2009.
  22. Heo BM, KH Ryu, "Prediction of prehypertension and hypertension based on anthropometry, blood parameters, and spirometry", IEEE Journal of Biomedical and Health Informatics, 2018. Under review.