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

TL;DR

This paper introduces a machine learning-based approach for verifying and validating computer models of thermal processes in breast cancer diagnosis, improving model accuracy using medical data analysis.

Contribution

It presents a novel method combining deep data analysis and machine learning to enhance the verification and validation of physical models in medical diagnostics.

Findings

Successful refinement of model parameters for thermal process dynamics in breast cancer.

Achieved high model adequacy aligning with medical measurement data.

Enhanced model reliability for breast cancer diagnosis applications.

Abstract

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.