An Empirical Evaluation of Clustering Techniques for the Oral Cancer Prediction


  • S. Sivakumar, T. Kamalakannan


Data Mining, K-means, Koho K-means, Kohonen Map, Clustering, Oral Cancer, R and Python


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.


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How to Cite

T. Kamalakannan, S. S. . (2024). An Empirical Evaluation of Clustering Techniques for the Oral Cancer Prediction. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 205–210. Retrieved from



Research Article