应用数据挖掘技术从大脑胶质瘤病例中获取诊断知识

Data Mining in Diagnostic Knowledge Acquisition from Patients with Brain Glioma

采用数据挖掘技术中 3种主要算法 :多层感知器网络、决策树以及规则提取从大脑胶质瘤病例中获取胶质瘤恶性程度的术前诊断知识。对于测试样本 ,它们的平均准确率都超过了 80 % ,达到了医生的一般要求。如果准确率是诊断中首要考虑的因素 ,那么隐层节点数较小且直接利用数值属性的多层感知器网络具有最好的性能。如果要对获取的诊断知识进行人工整理 。

In order to correctly predict the malignant degree of brain glioma, three data mining algorithms: multi-layer perceptron network(MLP), decision tree, and rule induction are adopted to acquire diagnostic knowledge from patients with brain glioma cases. Totally 280 cases are collected, and some of them contain missing values. Preprocessing is taken to make them applicable to all three algorithms. Performance comparisons are carried out with a 10-fold cross validation test. Although the result of MLP is hard to be understood and cannot be applied directly, its reliability and accuracy are the highest when only a few hidden nodes are involved. Unlike MLP, both decision tree and rule induction use attribute-value pairs to represent diagnostic knowledge derived from treated cases. These could improve both the understandability and applicability of their results. When compared with rule induction, the inherent restriction in structure makes decision tree more efficient in decision-making but meanwhile hurts its simplicity, accuracy, and reliability. For testing samples, results of all these algorithms can achieve accuracy rate over 80%, which satisfies the basic requirement of neuroradiologists. If diagnostic accuracy rate is the main factor to be considered, MLP with only a few hidden nodes is the best. If the result is expected to be further checked or evaluated, rule induction will be the best algorithm. This work proves that data mining techniques can be used to obtain valid diagnostic knowledge from brain glioma cases and make computer aided diagnosis system in this field feasible.