The use of machine learning methods in modern mathematical oncology

Abstract

The purpose of the study is to analyze modern approaches to assessing the efficacy and safety of anti-cancer drugs using machine learning methods, as well as to determine the prospects for their use in modern mathematical oncology, in oncological research that widely uses mathematical modelling and simulation.

As a result of the study, a literature review was conducted on the use of machine learning in oncology. An analysis of the use of the main machine learning methods, such as Supervised Learning (SL), Unsupervised Learning (UL), and Reinforcement Learning (RL) in modern oncology was performed. Examples of the use of various machine learning algorithms in research related to anti-cancer therapy and oncology in general are given. The advantages and disadvantages of the used machine learning algorithms are analyzed depending on the tasks to be solved.

Conclusions: Machine learning methods are already widely used in medical research in the field of oncology.  They have been successfully applied to solve many issues and showed good results, but there are still many areas in oncology where the use of machine learning methods can make a significant contribution to improving medical research and medical care in the treatment of cancer. For example, the use of algorithms based on reinforcement learning in precision medicine looks very promising, as its methods play a significant role in the personalized treatment of cancer.