A Platform of Biomedical Literature Mining for Categorization of Cancer Related Abstracts

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.