Application of computer simulation results and machine learning in analysis of microwave radiothermometry data

This work was done with the aim of developing the fundamental breast cancer early differential diagnosis foundations based on modeling the space-time temperature distribution using the microwave radiothermometry method and obtained data intelligent analysis. The article deals with the machine learning application in the microwave radiothermometry data analysis. The problems associated with the construction mammary glands temperature fields computer models for patients with various diagnostics classes, are also discussed. With the help of a computer experiment, based on the machine learning algorithms set (logistic regression, naive Bayesian classifier, support vector machine, decision tree, gradient boosting, K-nearest neighbors, etc.) usage, the mammary glands temperature fields computer models set adequacy.

Verification and Validation of Computer Models for Diagnosing Breast Cancer Based on Machine Learning for Medical Data Analysis

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.