doi: 10.4304/jsw.6.12.2515-2520
Mining a Small Medical Data Set by Integrating the Decision Tree and <i>t</i>-test
- 1 Department of Obstetrics and Gynecology, Chang Gung Memorial Hospital, Taipei, Taiwan 25137, R.O.C.
- 2 Department of Computer Science & Information Engineering, Tamkang University, Tamsui, Taipei County, Taiwan 25137, R.O.C.
- 3 Department of Information & Communication, Tamkang University, Tamsui, Taipei County, Taiwan 25137, R.O.C.
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.
Keywords
- Data mining
- Decision tree
- t-test
- p-va
How to Cite
Ming-Yang Chang, Chien-Chou Shih, Ding-An Chiang, and Chun-Chi Chen, "Mining a Small Medical Data Set by Integrating the Decision Tree and <i>t</i>-test," Journal of Software, vol. 6, no. 12, pp. 2515-2520, 2011. https://doi.org/ 10.4304/jsw.6.12.2515-2520
Copyright & License
Copyright © 2011 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).