A Hybrid Approach of Association Rule & Hidden Makov Model to Improve Efficiency Medical Text Classification

Abstract

Abstract:Text classification problem is a set of documents be classified into a predefined set of categories, each document is classified based on a set of features (words). However, some of the words not relevant to a category which causes a gap between words relevance in a document. A lot of research articles in public databases, and The digitization of critical medical information such as lab reports, patients records, research papers, and anatomic images tremendous amounts of biomedical research data are generated every day. So that, the classification this data and retrieving information relevant to information users' needs have been a primary research issue in the field of Information Retrieval, and the adoption of classification has been applied to tackle this particular problem. In this paper, we propose a hybrid model for the classification of biomedical texts according to their content, using Association Rules and Hidden Markov Model as classifier. In order to demonstrate it, we present a set of experiments performed on OHSUMED biomedical text corpora. Our classifier compared with Naive Bayes and Support Vector Machine models. The evaluation result shows that the proposed classification is complete and accurate when compared with Naive Bayes and Support Vector Machine models.