On interrelation of some signs of RTM diagnostics of mammary glands diseases

The article is devoted to the development of mining methods and interpreting medical thermometric data for breast diseases diagnostics. The methods of applied statistics and artificial intelligence are successfully used to solve problems in medical diagnostics, monitoring and forecasting. The problem of medical diagnostics has traditionally developed two classes of systems, and different methods are put into their base. One class consists of systems based on statistical models, and the other one—on mathematical ones. The foundation of the latter is represented by mathematical algorithms involved in the search. There is usually a partial correspondence between the symptoms of another patient and the symptoms previously observed patients, the diagnoses of which are known. The knowledge of experts is at the core systems of the second class. These algorithms operate on the patient information and knowledge on diseases, presented in a form more or less close to the views of doctors (and described by expert physicians), which is achieved due to the implicit or explicit use of ontologies of medical diagnostics.