基于神经网络的慢性病危险因素人体模型的建模方法

doi:10.3969/j.issn.0258-8021. 2014. 06.011

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Abstract The aim of this study is to set up an individualized modeling method, based on the back propagation (BP) learning algorithm on an artificial neural network (ANN), for a risk factor control of chronic disease. The inputs of the model include the parameters about physical activity, food intake (salt, grains, vegetables, fruits, meat and poultry, eggs, fish, legumes, milk, oil, animal internal organs), drinking and smoking. And the outputs are systolic pressure, diastolic pressure, glucose, heart rate and BMI. The reliability of the model was test by residual analysis. The result showed that coincidence rates of measured outputs with the estimated ones of 9 models were over 80% among 13 volunteers. So it is concluded that the proposed method is generally practical, providing a basis for the design of individualized state control of chronic disease risk factor.

Key words： chronic disease risk factor individualized model neural network state control

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	BAO Jia Ming¹WANG Shun²ZHU Chao Yang² WANG Yi Qing¹XU Ya Shi¹WANG Qiang^2

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BAO Jia Ming¹WANG Shun²ZHU Chao Yang² WANG Yi Qing¹XU Ya Shi¹WANG Qiang^2. A Human Body Modeling for Risk Factors of Chronic Disease with an Artificial Neural Network[J]. journal1, 2014, 33(6): 715-721.

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http://cjbme.csbme.org/EN/10.3969/j.issn.0258-8021. 2014. 06.011 OR http://cjbme.csbme.org/EN/Y2014/V33/I6/715

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