|Table of Contents|

[1] Ma Guangzhi, Lu Yansheng, Song Enmin, Nie Shaofa, et al. Neural network based online hypertension risk evaluation system [J]. Journal of Southeast University (English Edition), 2008, 24 (3): 267-271. [doi:10.3969/j.issn.1003-7985.2008.03.004]
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Neural network based online hypertension risk evaluation system()
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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:
24
Issue:
2008 3
Page:
267-271
Research Field:
Automation
Publishing date:
2008-09-30

Info

Title:
Neural network based online hypertension risk evaluation system
Author(s):
Ma Guangzhi Lu Yansheng Song Enmin Nie Shaofa Jing Weifeng Zhang Wei
College of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
Keywords:
hypertension prediction neural network information gain
PACS:
TP183
DOI:
10.3969/j.issn.1003-7985.2008.03.004
Abstract:
Since the previous research works are not synthetic and accurate enough for building a precise hypertension risk evaluation system, by ranking the significances for hypertension factors according to the information gains on 2 231 normotensive and 823 hypertensive samples, totally 42 different neural network models are built and tested.The prediction accuracy of a model whose inputs are 26 factors is found to be much higher than the 81.61% obtained by previous research work. The prediction matching rates of the model for “hypertension or not”, “systolic blood pressure”, and “diastolic blood pressure” are 95.79%, 98.22% and 98.41%, respectively.Based on the found model and the object oriented techniques, an online hypertension risk evaluation system is developed, being able to gather new samples, learn the new samples, and improve its prediction accuracy automatically.

References:

[1] Walker J, MacKenzie A D, Dunning J.Does reducing your salt intake make you live longer? [J].Interactive CardioVascular and Thoracic Surgery, 2007, 6(6):793-798.
[2] He F J, MacGregor A.Effect of modest salt reduction on blood pressure:a meta-analysis of randomized trials.Implications for public health[J].Journal of Human Hypertension, 2002, 16(1):761-770.
[3] Bacquer D D, Clays E, Delanghe J, et al.Epidemiological evidence for an association between habitual tea consumption and markers of chronic inflammation [J].Atherosclerosis, 2006, 189(2):428-435.
[4] Geleijnse J M, Kok F J, Grobbee D E.Impact of dietary and lifestyle factors on the prevalence of hypertension in Western populations[J].European Journal of Public Health, 2004, 14(3):235-239.
[5] Lisboa P J G.Neural networks in medical journals:current trends and implications for biopattern[C]//Proc of the First European Workshop on Assessment of Diagnostic Performance (EWADP).Milan, 2004:99-112.
[6] Poli R, Cagnoni S, Livi R, et al.A neural network expert system for diagnosing and treating hypertension[J].Computer, 1991, 24(3):64-71.
[7] Ning G, Su J, Li Y, et al.Artificial neural network based model for cardiovascular risk stratification in hypertension[J].Medical and Biological Engineering and Computing, 2006, 44(3):202-208.
[8] Kent J T.Information gain and a general measure of correlation [J].Biometrika, 1983, 70(1):163-73.
[9] Rumelhart D E, Hinton G E, Williams R J.Learning representations by back-propagating errors [J].Nature, 1986, 323(9):533-536.
[10] Rumelhart D E.Parallel distributed processing[M].Cambridge, MA:MIT Press, 1986:318-362.
[11] Vogl T P, Mangis J K.Accelerating the convergence of the back-propagation method [J].Biological Cybernetics, 1988, 59(3):256-264.
[12] Patric P.Minimization method for training feedforward neural networks[J].Neural Networks, 1994, 7(1):1-11.

Memo

Memo:
Biographies: Ma Guangzhi(1964—), male, associate professor, maguangzhi@hust.edu.cn;Lu Yansheng(corresponding author), male, professor, LYS@hust.edu.cn.
Foundation item: The National High Technology Research and Development Program of China(863 Program)(No.2006AA02Z347).
Citation: Ma Guangzhi, Lu Yansheng, Song Enmin, et al.Neural network based online hypertension risk evaluation system[J].Journal of Southeast University(English Edition), 2008, 24(3):267-271.
Last Update: 2008-09-20