Open AccessApplication of Computer Simulation Results and Machine Learning in the Analysis of Microwave Radiothermometry Data
Author(s) -
Maxim Polyakov,
Illarion Popov,
Alexander Losеv,
А. В. Хоперсков
Publication year - 2021
Publication title -
matematičeskaâ fizika i kompʹûternoe modelirovanie
Language(s) - English
Resource type - Journals
eISSN - 2587-6902
pISSN - 2587-6325
DOI - 10.15688/mpcm.jvolsu.2021.2.3
Subject(s) - decision tree , machine learning , computer science , artificial intelligence , support vector machine , gradient boosting , boosting (machine learning) , logistic regression , data mining , bayesian probability , random forest
This work was done with the aim of developing the fundamental breast cancer early differential diagnosis foundations based on modeling the spacetime 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, Knearest neighbors, etc.) usage, the mammary glands temperature fields computer models set adequacy.