This work was carried out as part of research on the development of methods for the intelligent analysis of medical thermometric data. These methods are designed to create a consultative intelligent system for the diagnosis of breast cancer. We propose a new approach to the formation of a feature space based on microwave radiothermometry data that are designed to detect malignant tumors. In the process, about 200 new signs were obtained and tested. Based on them, we created an algorithm for the localization of tumors in the mammary gland. This algorithm is a weighted voting algorithm that is configured using a genetic algorithm. The resulting localization algorithm can achieve an accuracy over 70% in test samples.

Keywords: microwave thermometry, breast cancer, mammology, tumor localization, accuracy, data mining, genetic algorithm, weighted voting algorithm, thermometric diagnostic features, cross-validation