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《第36届中国控制会议论文集(C)》2017年
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Disease Diagnostic Prediction Model Based on Improved Hybrid CAPSO-BP Algorithm

Zhenbing Yuan  Shuli Guo  Lina Han  
【摘要】:To improve the simulation accuracy of disease prediction model,a modified hybrid algorithm combining BP neural network(BPNN) with particle swarm optimization(PSO) algorithm based on chaos theory optimization is proposed considering BPNN is easy to fall into the local extremum.The chaos theory is used to optimize PSO algorithm to overcome the premature convergence of the traditional PSO algorithm.Then,the improved CAPSO algorithm is used to train the BPNN,to make full use of the global search characteristic of PSO algorithm and the local search ability of BPNN.The fitness function of the PSO algorithm is used as the energy function,and the optimization method of the improved hybrid algorithm is selected according to the specified number of evolution.An optimized network model was used to predict the prevalence of coronary heart disease.Compared with other algorithms such as BP neural network,the results show that the proposed algorithm has high accuracy and can significantly improve the quality of prediction.
【作者单位】:Key Laboratory of Intelligent Control and Decision,School of Automation,Beijing Institute of Technology Mathematical Institute,University of Oxford Department of Cardiology Internal Medicine,Nanlou Branch of Chinese PLA General Hospital
【基金】:supported by Hainan Province Science and Technology Cooperation Fund Project(KJKZ2015-36) Introduced and integrated demonstration projects of Hainan Province(NO.YJJC20130009)
【分类号】:R318;TP18
【正文快照】:
1Introduction With the aging of the world intensified,disease directly affects the quality of life of individuals and their family members as well,in addition to increasing the cost of health care.At the same time,hospitals and medical clinics have a l

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