【摘要】 贝叶斯网络(B ayes ian N etw ork)可以把统计学和图论有效地结合起来,近年来成为数据挖掘中的研究热点,其优点可以综合先验信息和样本信息,适于处理不完整数据集。本文采用280例病例作为训练数据,利用B ayes ian netw ork进行大脑胶质瘤高低度的自动诊断,利用严格的B ayes规则进行推理,在推理过程中采用了D分离来简化过程,其诊断正确率达到80%以上,达到了领域专家的要求,而且在可理解性方面要比多层感知器和决策树要好。更多还原

【Abstract】 Bayesian network connects graph theory with statistics,being an important research direction in data mining.Compared with other approaches used for data mining,Bayesian network can combine prior knowledge with observed data.Besides that,it can handle incomplete data sets.This paper applies Bayesian network to predict the malignant degree of brain glioma.Totally 280 cases are collected,and some of them contain missing values.Preprocessing is taken to make them applicable to the algorithms.Unlike MLP network,both Bayesian network and decision tree use attribute-value pairs to represent diagnostic knowledge derived from treated cases.These could improve both the understandability and applicability of their results.Results of all these algorithms can achieve accuracy rate over 80 %,which satisfies the requirement of neuroradiologists.更多还原