Issue |
MATEC Web of Conferences
Volume 150, 2018
Malaysia Technical Universities Conference on Engineering and Technology (MUCET 2017)
|
|
---|---|---|
Article Number | 06003 | |
Number of page(s) | 6 | |
Section | Information & Communication Technology (ICT), Science (SCI) & Mathematics (SM) | |
DOI | https://doi.org/10.1051/matecconf/201815006003 | |
Published online | 23 February 2018 |
Application of Data Mining Techniques for Medical Data Classification: A Review
1
Faculty of Computer Science and Information Technology
2
Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia, Batu Pahat, Malaysia
* Corresponding author: saima@uthm.edu.my
This paper investigates the existing practices and prospects of medical data classification based on data mining techniques. It highlights major advanced classification approaches used to enhance classification accuracy. Past research has provided literature on medical data classification using data mining techniques. From extensive literature analysis, it is found that data mining techniques are very effective for the task of classification. This paper analysed comparatively the current advancement in the classification of medical data. The findings of the study showed that the existing classification of medical data can be improved further. Nonetheless, there should be more research to ascertain and lessen the ambiguities for classification to gain better precision.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (http://creativecommons.org/licenses/by/4.0/).
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.