Prediction of fluctuations in a chaotic cancer model using machine learning

Abstract

Cancer is a group of diseases and the second leading cause of death according to World Health Organization. Mathematical and computational methods have been used to explore the cancer cells spread and the mechanism of their growth. We study a cancer model that exhibits both periodic and chaotic attractors. It describes the interactions among host, effector immune, and cancer cells. It is observed fluctuations in the population of cells. The fluctuation range can be associated with the appearance of tumour cells. In this work, we use machine learning algorithms for the prediction of fluctuations. We show that our machine learning classification is able to identify fluctuations that are associated with the growth rate of cancer cells.