[Data mining in diagnostic knowledge acquisition from @entity1 with XXXX ].

@entity1 @entity13 @entity14339
In order to correctly predict the malignant degree of @entity13 , three data mining algorithms: multi-layer perceptron network( @entity14339 ), decision tree, and rule induction are adopted to acquire diagnostic knowledge from @entity1 with @entity13 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 @entity14339 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 @entity14339 , 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, @entity14339 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 @entity13 cases and make computer aided diagnosis system in this field feasible.
