The application of machine learning methods in modern cancer therapy

Abstract

The purpose of this study is to analyze modern approaches to assessing the efficacy and safety of drugs used in anti-cancer therapy using machine learning methods. Particular attention is paid to the prospects of implementing such methods in mathematical oncology, a field that actively uses mathematical modeling and computer simulations in oncological research. As part of the study, we searched for and studied relevant scientific sources on the application of machine learning in oncology. As a result, a systematic review of the literature covering the use of machine learning in this area was conducted. The main machine learning approaches, such as Supervised Learning (SL), Unsupervised Learning (UL), and Reinforcement Learning (RL), are analyzed in the context of modern oncology. Specific examples of the use of various machine learning algorithms in research related to cancer treatment, as well as more general oncological problems, are considered. The advantages and limitations of these approaches are assessed depending on the goals set, for example, in such tasks as predicting treatment response, biomarker identification, and automated medical image analysis. The study found that machine learning is already being actively implemented in oncology research and demonstrates high efficiency in solving various problems, in particular, identifying hidden patterns that are not available in traditional analysis. At the same time, it is noted that there are numerous other areas where the use of machine learning can significantly enhance scientific research and clinical practice. In particular, the use of reinforcement learning algorithms in the field of personalized precision medicine, which plays a key role in creating individualized approaches to cancer treatment, looks promising.

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