Title :
A Platform of Biomedical Literature Mining for Categorization of Cancer Related Abstracts
Author :
Lee, Chung-Hong ; Chiu, Hui-Chuan ; Yang, Hsin-Chang
Author_Institution :
Nat. Kaohsiung Univ. of Appl. Sci., Kaohsiung
Abstract :
In this paper, we develop a platform framework for categorization of cancer related abstracts using support vector machines (SVMs) based text categorization techniques with a one-against-all (OAA) learning algorithm for classification decisions. The corpora for the work were selected from the Website of PubMed database. By using information derived from PubMed literature source, including topics of breast cancer, cervical cancer, gastric cancer, lung cancer, rectum cancer and esophagus cancer, we randomly selected 6,000 medical abstracts for implementing our system and performing experiments. The experimental results show that the platform model has potentials for categorization of multiple cancer related literature texts.
Keywords :
abstracting; cancer; data mining; medical information systems; pattern classification; support vector machines; text analysis; PubMed database; Website; biomedical literature mining; breast cancer; cancer related abstract categorization; cervical cancer; classification decisions; esophagus cancer; gastric cancer; lung cancer; one-against-all learning algorithm; rectum cancer; support vector machines; text categorization; Abstracts; Breast cancer; Cervical cancer; Classification algorithms; Databases; Lungs; Machine learning; Support vector machine classification; Support vector machines; Text categorization;
Conference_Titel :
Innovative Computing, Information and Control, 2007. ICICIC '07. Second International Conference on
Conference_Location :
Kumamoto
Print_ISBN :
0-7695-2882-1
DOI :
10.1109/ICICIC.2007.76
