Using data mining for assessing diagnosis of breast cancer

Author

Abdelaal, Medhat Mohamed Ahmed ; Farouq, Muhamed Wael ; Sena, Hala Abou ; Salem, Abdel-Badeeh Mohamed

Author_Institution

Stat. & Math. Dept., Ain Shams Univ., Cairo, Egypt

fYear

2010

fDate

18-20 Oct. 2010

Firstpage

11

Lastpage

17

Abstract

The capability of the classification SVM, Tree Boost and Tree Forest in analyzing the DDSM dataset was investigated for the extraction of the mammographic mass features along with age that discriminates true and false cases. In the present study, SVM technique shows promising results for increasing diagnostic accuracy of classifying the cases witnessed by the largest area under the ROC curve (area under empirical ROC curve =0.79768 and area under binomial ROC curve = 0.85323) comparable to empirical ROC and binomial ROC of 0.57575 and 0.58548 for tree forest while least empirical ROC and binomial ROC of 0.53452 and 0.53882 was accounted by tree boost. These results are confirmed by SVM average gain of 1.7323, tree forest average gain of 1.5576 and tree boost average gain of 1.5718.

Keywords

cancer; data mining; medical computing; support vector machines; trees (mathematics); SVM; breast cancer diagnosis; data mining; tree boost; tree forest; Barium; Classification tree analysis; Data mining; Frequency control; High definition video; Information technology; Breast Cancer; Classification Support Vector Machine (SVM); Decision Tree; Gain; Receiver Operating Characteristic Curve (ROC); Tree Boost; Tree Forest;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Science and Information Technology (IMCSIT), Proceedings of the 2010 International Multiconference on

Conference_Location

Wisla

ISSN

2157-5525

Print_ISBN

978-1-4244-6432-6

Type

conf

DOI

10.1109/IMCSIT.2010.5679647

Filename

5679647