International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

Frequency: 12
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 Volume 7, Issue 4 (April 2020), Pages: 1-8

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 Review Paper

 Title: Review of feature extraction approaches on biomedical text classification

 Author(s): Rozilawati Dollah 1, *, Tiara Izrinda Jafni 1, Haslina Hashim 1, Mohd Shahizan Othman 1, Abd Wahid Rasib 2

 Affiliation(s):

 1School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia
 2Program of Geoinformation, Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia

  Full Text - PDF          XML

 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0001-6007-1749

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2020.04.001

 Abstract:

The overcoming volume of online biomedical literature causes congestion of data and difficulties in organizing these documents and also to retrieve the required documents from the database, especially in the Medline database. One of the solutions to surpass the overwhelming of documents is to apply classification. However, each document must be represented by a set of terminology or feature vectors. The identification of terminology or feature from biomedical literature is one of the most important and challenging tasks in text classification. This is due to a large number of new features and entities that appear in the biomedical domain. In addition, combining sets of features from different terminological resources leads to naming conflicts such as homonymous use of names and terminological ambiguities. Therefore, the purpose of this research is to investigate and evaluate the effective ways for extracting the relevant and meaningful features in order to increase the classification accuracy and improve the performance of web searches. Towards this effort, we conduct several classification experiments to evaluate and compare the effectiveness of feature extraction approaches for extracting the relevant and informative features from the biomedical literature. For our experiments, we use two different sets of features, which are a set of features that are extracted using the Genia tagger tool and set of features that are extracted by medical experts from Pusat Perubatan Universiti Kebangsaan Malaysia (PPUKM). The results show the performance of classification using features that are extracted by medical experts outperform the performance of classification using the Genia Tagger tool when applying feature selection method. 

 © 2020 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: Biomedical literature, Feature extraction, Feature selection, Text classification, Text mining

 Article History: Received 26 September 2019, Received in revised form 10 January 2020, Accepted 12 January 2020

 Acknowledgment:

This study is supported by the Fundamental Research Grant Scheme (FRGS) under the Vote No. 4F559 that sponsored by the Ministry of Higher Education (MOHE) and Research University Grant Scheme (RUG) under the Vote No. 13J94 and 20H01. The authors are greatly obliged to Universiti Teknologi Malaysia (UTM) and Information Engineering and Behavioral Informatics (INFOBEE) Research Group for support and motivation.

 Compliance with ethical standards

 Conflict of interest: The authors declare that they have no conflict of interest.

 Citation:

 Dollah R, Jafni TI, and Hashim H et al. (2020). Review of feature extraction approaches on biomedical text classification. International Journal of Advanced and Applied Sciences, 7(4): 1-8

 Permanent Link to this page

 Figures

 Fig. 1 Fig. 2 Fig. 3

 Tables

 Table 1 Table 2

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