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This paper presents an analysis of the prediction of survivability rate of breast cancer patients using data mining techniques. The data used is the SEER Public-Use Data. The preprocessed data set consists of 151,886 records, which have all the available 16 fields from the SEER database. The paper has investigated three data mining techniques: the Naive Bayes, the back-propagated neural network, and the C4.5 decision tree algorithms. Several experiments were conducted using these algorithms. The achieved prediction performances are comparable to existing techniques. However, the paper found out that C4.5 algorithm has a much better performance than the other two techniques.

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Format:PDFSize:55 KB
Date:Feb 2006
Pages:4

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