DOI: https://doi.org/10.18517/ijods.1.2.114-119.2020

Classification of Biomedical Literature in Hypertension and Diabetes

Nur Aniq Syafiq Rodzuan (1) , Shahreen Kasim (2) , Mohanavali Sithambranathan (3) , Muhammad Zaki Hassan (4)
(1) Faculty of Computer Science and Technology, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Johor, Malaysia.
(2) Faculty of Computer Science and Technology, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Johor, Malaysia.
(3) Faculty of Computer Science and Technology, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Johor, Malaysia.
(4) Faculty of Computer Science and Technology, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja, Johor, Malaysia.
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Keywords:
classification, biomedical literature, hypertension, diabetes

Abstract

Textual information gives us more clear information as it is presented using words and characters, which is easy for humans to understand. To extract this kind of information, text mining was introduced as new technology. Text mining is the process of extracting non-trivial patterns or knowledge from text documents or from textual databases. The purpose of this research paper is to perform and compare keyword extraction using statistical and linguistic extraction tools for 120 text documents related to hypertension and diabetes disease. In order to draw this comparison, RStudio, a statistical-based tool and TerMine, a linguistic-based tool have been used to demonstrate the process of extracting the specified keyword from the biomedical literature. Thus, classification evaluation using Naïve Bayes classifier is carried out in order to evaluate and compare the performance of the statistical and linguistic approaches using these tools. Experimental results show the result of the comparison and the difference between both tools in executing extraction keywords.

Article Details

How to Cite
[1]
N. A. S. Rodzuan, S. Kasim, M. Sithambranathan, and M. Z. Hassan, “Classification of Biomedical Literature in Hypertension and Diabetes”, Int. J. Data. Science., vol. 1, no. 2, pp. 114-119, Aug. 2020.
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Vol 1 No 2 (2020)
Section
Articles
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