Computer Engineering and Applications ›› 2018, Vol. 54 ›› Issue (1): 159-165.DOI: 10.3778/j.issn.1002-8331.1607-0137

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Analysis method of heart disease for pervasive health monitoring

ZHU Xuetong1, QIAN Xiujuan2, SUN Weiwei1, HOU Xianchun1, CHEN Guangsheng3, WANG Yongli4   

  1. 1.College of Science, Jiamusi University, Jiamusi, Heilongjiang 154007, China
    2.China Mobile (Hangzhou) Information Technology Co., Ltd., Hangzhou 310012, China
    3.Fuel Branch, Jiamusi Power Plant, Jiamusi, Heilongjiang 154007, China
    4.School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
  • Online:2018-01-01 Published:2018-01-15

面向普适健康监测的心脏病分析方法

朱雪彤1,钱秀娟2,孙薇薇1,侯宪春1,陈广生3,王永利4   

  1. 1.佳木斯大学 理学院,黑龙江 佳木斯 154007
    2.中移(杭州)信息技术有限公司,杭州 310012
    3.佳木斯发电厂 燃料分厂,黑龙江 佳木斯 154007
    4.南京理工大学 计算机科学与工程学院,南京 210094

Abstract: In order to promote the health-care quality of pervasive health monitoring service, this paper uses Bayesian networks to analyze heart disease, and presents a method to conform the sequence of network nodes from sample dataset. This method overcomes the limitation that traditional algorithms which require experts of medical field give the sequence of network nodes. In addition, in order to shorten the analysis time, a parallel optimization technique is adopted to accelerate the establishment of HD diagnosis model over large amounts of data. Experiments show that the proposed method can improve the accuracy of the modeling and shorten the modeling time to some extent.

Key words: pervasive health monitoring, heart disease analysis, Bayesian networks, parallel optimization

摘要: 为了提高普适健康监测服务的病人护理质量,采用贝叶斯网络对心脏病数据进行及时准确的分析,提出从样本数据集中学习网络节点顺序的方法,克服了传统算法需要领域专家给定网络中节点顺序的限制;另外,引入了并行优化方法进一步提高在大数据量情况下建立心脏病诊断分析模型的速度。实验证明提出的方法在一定程度上提高了模型分析的准确率,并且缩短了建模的时间。

关键词: 普适健康监测, 心脏病分析, 贝叶斯网络, 并行优化