Identifying Contextual Information in Document Classification using Term Weighting

Document classification particularly in biomedical research plays a vital role in extracting knowledge from medical literature, journal, article and report. To extract meaningful information such as signs, symptoms, diagnoses and treatments of any disease by classification, the context needs to be considered. The need to automatically extract key information from medical text has been widely accepted and it has been proven that search based approaches are limited in their ability. This paper presents a novel method of information identification for a particular disease using Gaussian Naïve Bayes and feature weighting approach that is then classified by the context. It is useful to enhance the effectiveness of analytics by considering the importance of the term as well as the probability of every feature of the disease during classification. Experimental results show that our method upgrades performance of classification system and is an improvement from traditional classification system.