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