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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/54222

    Title: Mining a Small Medical Data Set by Integrating the Decision Tree and t-test
    Authors: Chang, Ming-Yang;Shih, Chien-Chou;Chiang, Ding-An;Chen, Chun-Chi
    Contributors: 淡江大學資訊傳播學系;淡江大學資訊工程學系
    Keywords: Data mining;Decision tree;t-test;p-value;Ovarian endometriomas
    Date: 2011-12
    Issue Date: 2011-07-03 00:45:20 (UTC+8)
    Publisher: Oulu: Academy Publisher
    Abstract: Although several researchers have used statistical methods to prove that aspiration followed by the injection of 95% ethanol left in situ (retention) is an effective treatment for ovarian endometriomas, very few discuss the different conditions that could generate different recovery rates for the patients. Therefore, this study adopts the statistical method and decision tree techniques together to analyze the postoperative status of ovarian endometriosis patients under different conditions. Since our collected data set is small, containing only 212 records, we use all of these data as the training data. Therefore, instead of using a resultant tree to generate rules directly, we use the value of each node as a cut point to generate all possible rules from the tree first. Then, using t-test, we verify the rules to discover some useful description rules after all possible rules from the tree have been generated. Experimental results show that our approach can find some new interesting knowledge about recurrent ovarian endometriomas under different conditions.
    Relation: Journal of Software 6(12), pp.2515-2520
    DOI: 10.4304/jsw.6.12.2515-2520
    Appears in Collections:[Graduate Institute & Department of Information and Communication] Journal Article
    [Graduate Institute & Department of Computer Science and Information Engineering] Journal Article

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