Classification methods in the diagnosis of breast cancer

This paper concerns the problem of diagnosing the type of cancer with the use of machine learning and statistical methods. Nowadays, the problem of neoplasms, in particular breast cancer, is one of humanity's greatest challenges. The identification of cancer and its type is extremely important. In solving this problem, classification methods can be used as objective tools that may be helpful for doctors making a diagnosis. For this reason, we discuss many efficient classifiers in the context of cancer detection. In addition, we consider the topic of data set transformations to deal with the problem of data unbalance, as well as measures of classification quality. In the experimental part, an attempt will be made to find the best classifier and to improve the quality of the original data set to obtain the highest values of classification quality measures for a particular data set.