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Lung Cancer Incidence Prediction Using Machine Learning Algorithms

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.