Evaluating the risk of hypertension using an artificial neural network method in rural residents over the age of 35 years in a Chinese area

Hypertension Research

Volumn 33, Issue 7, 2010, Pages 722-726

  Huang, Shuqiong ^a   Xu, Yihua ^a   Yue, Li ^a   Wei, Sheng ^a   Liu, Li ^a   Gan, Xiumin ^a   Zhou, Shuihong ^a   Nie, Shaofa ^a  

^a HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

Author keywords

prediction model; risk factors; rural

Indexed keywords

ADULT; ALCOHOL CONSUMPTION; ARTICLE; ARTIFICIAL NEURAL NETWORK; CHINESE; CONTROLLED STUDY; DIET; EDUCATIONAL STATUS; EXERCISE; FAMILY HISTORY; FEMALE; FOOD INTAKE; HUMAN; HYPERTENSION; MAJOR CLINICAL STUDY; MALE; OBESITY; PREDICTION; PROBABILITY; RISK ASSESSMENT; RISK FACTOR; RURAL POPULATION; SITTING; AGED; CHINA; CROSS-SECTIONAL STUDY; HEALTH; MIDDLE AGED; OVERWEIGHT; PROGNOSIS; SEDENTARY LIFESTYLE; STATISTICS AND NUMERICAL DATA;

ADULT; AGED; CHINA; CROSS-SECTIONAL STUDIES; EDUCATIONAL STATUS; EXERCISE; FEMALE; HUMANS; HYPERTENSION; MALE; MIDDLE AGED; NEURAL NETWORKS (COMPUTER); OVERWEIGHT; PROGNOSIS; RISK FACTORS; RURAL HEALTH; SEDENTARY LIFESTYLE;

EID: 77954353115     PISSN: 09169636     EISSN: 13484214     Source Type: Journal    
DOI: 10.1038/hr.2010.73     Document Type: Article

References (40)

1. Lewington S, Clarke R, Qizilbash N, Peto R, Collins R. Age-specific relevance of usual blood pressure to vascular mortality: a meta-analysis of individual data for one million adults in 61 prospective studies. Lancet 2002; 360: 1903-1913.
2. China Hypertension League, Revising Committee for The Guidelines. Guidelines for the management of hypertension of China. Health Ministry of People's Republic of China: Beijing, 2005.
3. Gu DF, Reynolds K, Wu XG, Chen J, Duan XF, Muntner P, Huang GY, Reynolds RF, Su SY, Whelton PK, He J. Prevalence, awareness, treatment, and control of hypertension in China. Hypertension 2002; 40: 920-927.
4. Kaplan NM. Anxiety-induced hyperventilation\A common cause of symptoms in patients with hypertension. Arch Intern Med 1997; 157: 945-948.
5. Arima H, Yonemoto K, Doi Y, Ninomiya T, Hata J, Tanizaki Y, Fukuhara M, Matsumura K, Iida M, Kiyohara Y. Development and validation of a cardiovascular risk prediction model for Japanese: the Hisayama study. Hypertens Res 2009; 32: 1119-1122.
6. Gueyffier F, Boissel JP, Pocock S, Boutitie F, Coope J, Cutler J, Ekbom T, Fagard R, Friedman L, Kerlikowske K, Perry M, Prineas R, Schron E. Identification of risk factors in hypertensive patients: contribution of randomized controlled trials through an individual patient database. Circulation 1999; 100: e88-e94.
7. Chiong JR. Controlling hypertension from a public health perspective. Int J Cardiol 2008; 127: 151-156.
8. Vasan RS, Larson MG, Leip EP, Kannel WB, Levy D. Assessment of frequency of progression to hypertension in non-hypertensive participants in the Framingham Heart Study: a cohort study. Lancet 2001; 358: 1682-1686.
9. Pang WY, Sun ZQ, Zheng LQ, Li J, Zhang XG, Liu SS, Xu CL, Li JJ, Hu DY, Sun YX. Body mass index and the prevalence of prehypertension and hypertension in a Chinese rural population. Int Med 2008; 47: 893-897.
10. Vasan RS, Beiser A, Seshadri S, Larson MG, Kannel WB, D'Agostino RB, Levy D. Residual lifetime risk for developing hypertension in middle-aged women and men\The Framingham Heart Study. JAMA 2002; 287: 1003-1010. (Pubitemid 34252058)
11. Lin SG, Cheng TO, Liu XQ, Mai JZ, Rao XX, Gao XM, Deng HH, Shi ML. Impact of dysglycemia, body mass index, and waist-to-hip ratio on the prevalence of systemic hypertension in a lean Chinese population. Am J Cardio 2006; 97: 839-842.
12. Ueshima H, Zhang XH, Choudhury SR. Epidemiology of hypertension in China and Japan. J Hum Hyperten 2000; 14: 765-769.
13. Yu Z, Nissnien A, Vartiainen E, Yu ZJ, Nissinen A, Vartiainen E, Hu G, Tian HG, Guo ZY. Socio-economic status and serum lipids: across-sectional study in a Chinese urban population. J Clin Epidemiol 2002; 55: 143-149.
14. Wildman RP, Gu DF, Muntner P, Huang GY, Chen JC, Duan XF, He J. Alcohol intake and hypertension subtypes in Chinese men. J Hypertension 2005; 23: 737-743.
15. World Bank Sharing Rising Income: Disparities in China. World Bank: Washington, DC, USA 1997.
16. Sun ZQ, Zheng LQ, Detrano R, Zhang DY, Zhang XG, Xu CL, Li J, Liu SS, Li JJ, Hu DY, Sun Y. The accelerating epidemic of hypertension among rural Chinese women: Results from Liaoning Province. Am J Hypertens 2008; 21: 784-788.
17. Rose GA, Blackburn H, Gillum RF, Prinneas RJ. Cardiovascular survey methods. World Health Organization: Geneva, 1982.
18. Weiner JS, Lourie JA. Practical human biology. Academic Press: London, 1981.
19. Zhou BF. Predictive values of body mass index and waist circumference for risk factors of certain related diseases in Chinese adults\study on optimal cut-off points of body mass index and waist circumference in Chinese adults. Biomed Environ Sci 2002; 15: 83-96.
20. Adams KF, Schatzkin A, Harris TB, Kipnis V, Mouw T, Barbash RB, Hollenbeck A, Leitzmann MF. Overweight, obesity, and mortality in a large prospective cohort of persons 50 to 71 years old. N Engl J Med 2006; 355: 763-778.
21. Community intervention trial for smoking cessation (COMMIT): II. Changes in adult cigarette smoking prevalence. Am J Public Health 1995; 85: 193-200.
22. Jousilahti P, Tuomilehto J, Korhonen HJ, Vartiainen E, Puska P, Nissinen A. Trends in cardiovascular disease risk factor clustering in eastern Finland: results of 15-year follow-up of the North Karelia Project. Prev Med 1994; 23: 6-14.
23. Vartiainen E, Paavola M, McAlister A, Puska P. Fifteen-year follow-up of smoking prevention effects in the North Karelia youth project. Am J Public Health 1998; 88: 81-85.
24. BRFSS Questionnaires. 2003 (Accessed 10 January 2008, at http://www.cdc.gov/brfss/questionnaires/pdf-ques/2003brfss.pdf.).
25. Bell AC, Adair LS, Popkin BM. Understanding the role of mediating risk factors and proxy effects in the association between socio-economic status and untreated hypertension. Soc Sci Med 2004; 59: 275-283.
26. Naimi TS, Brown DW, Brewer RD, Giles WH, Mensah G, Serdula MK, Mokdad AH, Hungerford DW, Lando J, Naimi S, Stroup DF. Cardiovascular risk factors and confounders among nondrinking and moderate-drinking U.S. adults. Am J Prev Med 2005; 28: 369-373.
27. Mukamal KJ, Ding EL, Djousse L. Alcohol consumption, physical activity, and chronic disease risk factors: a population-based cross-sectional survey. BMC Public Health 2006; 6: 118.
28. Gurney K. An Introduction to Neural Networks. Routledge: London, 1997.
29. Duh MS, Wallker A, Ayanian JZ. Epidemiologic interpretation of artificial neural network. Am J Epidemiol 1998; 14: 464-471.
30. Grossi E. How artificial intelligence tools can be used to assess individual patient risk in cardiovascular disease: problems with the current methods. BMC Cardiovascular Disorders 2006; 6: 20.
31. Montie JE, Wei JT. Artificial neural networks for prostate carcinoma risk assessment: An overview. Cancer 2000; 88: 2655-2660.
32. Gary LG, Ruyun J. Receiver operating characteristic curve analysis of clinical risk models. Ann Thorac Surg 2001; 72: 323-326.
33. Hou XH. Urban-Rural disparity of overweight, hypertension, undiagnosed hypertension, and untreated hypertension in China. Asia-Pacific J Public Health Volume 2008; 20: 159-169.
34. Zheng L, Sun Z, Zhang X, Xu C, Li J, Hu D, Sun Y. Predictors of progression from prehypertension to hypertension among rural Chinese adults: results from Liaoning Province. Eur J Cardiovasc Prev Rehabil 2010; 17: 217-222.
35. Zhu J, Zhu XD, Liang SX, Xu ZY, Zhao JD, Huang QF, Wang AY, Chen L, Fu XL, Jiang GL. Prediction of radiation induced liver disease using artificial neural networks. Jpn J Clin Oncol 2006; 36: 783-788.
36. Baldassarre D, Grossi E, Buscema M, Intraligi M, Amato M, Tremoli E, Pustina L, Castelnuovo S, Sanvito S, Gerosa L, Sirtori CR. Recognition of patients with cardiovascular disease by artificial neural networks. Ann Med 2004; 36: 630-640.
37. Santos-García G, Varela G, Novoa N, Jimenez MF. Prediction of postoperative morbidity after lung resection using an artificial neural network ensemble. Artif Intell Med 2004; 30: 61-69.
38. Sato F, Shimada Y, Selaru FM, Shibata D, Maeda M, Watanabe G, Mori Y, Stass SA, Imamura M, Meltzer SJ. Prediction of survival in patients with esophageal carcinoma using artificial neural networks. Cancer 2005; 103: 1596-1605.
39. Matsui Y, Egawa S, Tsukayama C, Terai A, Kuwao S, Baba S, Arai Y. Artificial neural network analysis for predicting pathological stage of clinically localized prostate cancer in the Japanese population. Jpn J Clin Oncol 2002; 32: 530-535.
40. Agyemang C, van Hooijdonk C, Wendel-Vos W, Ujcic-Voortman JK, Lindeman E, Stronks K, Droomers M. Ethnic differences in the effect of environmental stressors on blood pressure and hypertension in the Netherlands. BMC Public Health 2007; 7: 147.