Аннотация:In this work, we developed a method for classification of RS and RR cancer cells based on the analysis of experimental data on clonogenic survival of cancer cells using machine learning. To determine the parameters of cell survival, the experimental dose-effect data were approximated by the linear-quadratic (LQ) model widely used in radiobiology. The dataset included 60 cancer cell lines of different types of cancer such as human pancreatic cancer, colon cancer, lung cancer, breast cancer and others. The dataset included radiosensitive cell lines like Capan-2, DanG (pancreatic cancer), MCF-7, ZR-751 (breast cancer), as well as radioresistance cell lines like suit-2 007, patu-8998T (pancreatic cancer), HPDE (human pancreatic duct epithelial cell line), BT-20 (breast cancer), and others. As a result of experimental data approximation by the LQ model, two parameters 𝛼 and 𝛽 were determined, the ratio of which is commonly used in radiobiology to evaluate the radiosensitivity of cancer cells. A high value of the 𝛼/𝛽 ratio is characteristic of RS cells, which have a low ability to repair damage after IR and vice versa. In order to increase the reliability of discrimination between RS and RR cells according to clonogenic survival data, a combination of the k-means and hierarchal clustering methods along with the principal component analysis was applied. Based on the obtained results, a statistical model was developed and trained on a dataset of experimental data to determine the radiosensitive and radioresistance cancer cells and was successfully validated using the new dataset of parameters α/β and α of cells which were not included in the training dataset.