The Category Forecast Model of Complete Blood Count Based on the Principal Components Analysis and BP Network

摘要：

In this paper we built a model using the principal components analysis (PCA) and BP neural network, to forecast the category of the complete blood count (CBC) category forecast problem. Two forecast methods were used to discuss, one was a pure BP model, another was a combined model with PCA and BP model, and the experiment results showed that, the accuracy of the former model was 64％, and the latter was 81.3％. So the combined model could improve the accuracy significantly.

关键词： Principal Component Analysis BP Network Complete Blood Count Category Forecast

作者: LIU Guangchen SUN Zhengshun SONG Mei

作者单位: School of Mathematics & Information, Ludong University, P.R.China, 264025

会议类型: 国际会议

会议名称: 2009 International Institute of Applied Statistics Studies(2009 国际应用统计学术研讨会)

会议地点: 青岛

会议语种:英文

页码: 1-4

在线出版日期: 2009-07-25（万方平台首次上网日期，不代表论文的发表时间）