  The method of microwave radiometry is one of the areas of medical diagnosis
of breast cancer. It is based on analysis of the spatial distribution of
internal and surface tissue temperatures, which are measured in the microwave
(RTM) and infrared (IR) ranges. Complex mathematical and computer models
describing complex physical and biological processes within biotissue increase
the efficiency of this method. Physical and biological processes are related to
temperature dynamics and microwave electromagnetic radiation. Verification and
validation of the numerical model is a key challenge to ensure consistency with
medical big data. These data are obtained by medical measurements of patients.
We present an original approach to verification and validation of simulation
models of physical processes in biological tissues. Our approach is based on
deep analysis of medical data and we use machine learning algorithms. We have
achieved impressive success for the model of dynamics of thermal processes in a
breast with cancer foci. This method allows us to carry out a significant
refinement of almost all parameters of the mathematical model in order to
achieve the maximum possible adequacy.
