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Lung Cancer Incidence Prediction Using Machine Learning Algorithms

Kubra Tuncal, Boran Sekeroglu, and Cagri Ozkan
Near East University, Information Systems Engineering, Nicosia, TRNC, Mersin 10, Turkey

Abstract—Everyday, the frequency of incidence of cancer disease is rising. It is one of the most fatal diseases in the world with several types and there is a few reliable data about incidence and mortality rates of cancer and its types. Thus, the prediction of the rates becomes challenging task for human beings. For this reason, several machine learning algorithms have been proposed to provide effective and rapid prediction of uncertain raw data with minimized error. In this paper, Support Vector Regression, Backpropagation Learning Algorithm and Long-Short Term Memory Network is used to perform lung cancer incidence prediction for ten European countries those records have been started from 1970. Results show that the prediction of incidence rates is possible with high scores with all algorithms; however, Support Vector Regression performed superior results than other considered algorithms.
Index Terms—lung cancer, support vector regression, backpropagation, long-short term memory

Cite: Kubra Tuncal, Boran Sekeroglu, and Cagri Ozkan, "Lung Cancer Incidence Prediction Using Machine Learning Algorithms," Journal of Advances in Information Technology, Vol. 11, No. 2, pp. 91-96, May 2020. doi: 10.12720/jait.11.2.91-96

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License (CC BY-NC-ND 4.0), which permits use, distribution and reproduction in any medium, provided that the article is properly cited, the use is non-commercial and no modifications or adaptations are made.