An Empirical Evaluation of Clustering Techniques for the Oral Cancer Prediction
Keywords:
Data Mining, K-means, Koho K-means, Kohonen Map, Clustering, Oral Cancer, R and PythonAbstract
Data Mining is now widely used in healthcare applications to predict various cancers such as breast, kidney, thyroid, Colorectal, ovarian and many others. Clustering in Data Mining offers a solution for determining the prediction of Oral Cancer. This research explores K-means algorithm and introduces a new novel algorithm, the Kohonen map with K-means (Koho K-means). The experimental findings are based on 3004 oral cancer datasets, focusing on the time complexity and accuracy of the algorithms. The comparative study is then conducted with varying cluster points. The experimental results prove that Koho K-means outperforms K-means in predicting oral cancer, particularly in terms of accuracy.
Downloads
References
Lavanya L and Dr. Chandra J, Oral Cancer Analysis Using Machine Learning Techniques, International Journal of Engineering Research and Technology, Volume 12, Number 5 (2019), pp.596-601.
Songul Cinaroglu, Integrated k-means clustering with data envelopment analysis of public hospital efficiency, Health Care Management Science, Springer, pp. 325-338, 2020.
K. Lalithamani, A. Punitha, A Machine Learning Approach for Oral Cancer Detection Using Enhanced Multi-Layer Perceptron, International Journal of Innovative Research in Applied Sciences and Engineering, Volume 2, Issue 8, pp. 319-331, February 2019.
Rui Máximo Esteves, Thomas Hacker and Chunming Rong, Competitive K-Means, a New Accurate and Distributed K-Means Algorithm for Large Datasets, IEEE Explore, 2014. DOI: 10.1109/CloudCom.2013.89
.Fernando Bação, Victor Lobo1, and Marco Painho, Self-organizing Maps as Substitutes for K-Means Clustering ,Springer, LNTCS,Volume 3516, 2005.
Akanksha Kapoor and Abhishek Singhal, A Comparative Study of K-Means, K-Means++ and Fuzzy C- Means Clustering Algorithms, IEEE Explore,2017. DOI:10.1109/CIACT.2017.7977272
] R. Prabhakaran, J. Mohana, Detection of Oral Cancer Using Machine Learning Classification Methods, International Journal of Electrical Engineering and Technology, Volume 11(3), 2020, pp. 384-393.
.Shilpa Harnale , Dhananjay Maktedar, Oral Cancer Detection: Hybrid Method of KFCM Clustering, International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume 8 ,Issue 5, January 2020.
Noor Kadhim Ayoob, Breast Cancer Diagnosis Using K-means Methodology, Journal of Babylon University, Pure and Applied Sciences, Number (1), Volume (26), 2018.
Velmurugan T, Efficiency of K-means and K-medoids Algorithms for Clustering Arbitrary Data Points, International Journal of Computer Technology & Technology, Volume 3(5), pp. 1758-1764, 2012.
.World Health Organization:https://www.who.int/team/noncommunicable-diseases/global-status-report-on-oral-health-2022.
The Indian Council of Medical Research: https://main.icmr.nic.in/
Alka Kumari and Megha Kamble, Improved Clustering Methodology for Lung Cancer Disease Prediction, International Journal of LNCT, Volume 4, Issue 16, February 2020.
Fatihah Mohd, Zainab Abu Bakar, Noor Maizura Mohamad Noor, Zainul Ahmad Rajion, Data preparation for pre-processing on oral cancer dataset, IEEE Explore 2013. DOI: 10.1109/ICCAS.2013.6703916.
S.Sivakumar and T.Kamalakannan, Performance based Analysis of K-Medoids and K-Means Algorithms for the Diagnosis and Prediction of Oral Cancer, Computational Intelligence for Clinical Diagnosis, Springer, pp. 215-226, 2023.
Arushi Tetarbe , Tanupriya Choudhury ,Teoh Teik Toe and Seema Rawat, Oral cancer detection using data mining tool ,IEEE Explore, pp. 35-39, 2017. ISBN:978-1-5386-1145-6
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
