Version 1
: Received: 27 January 2024 / Approved: 28 January 2024 / Online: 29 January 2024 (09:52:17 CET)
How to cite:
Kostyuchenko, E.; Amletova, E. Study of the Influence of Data Volume on the Quality of Regression to Restore the Distribution of Temperatures inside Tissue during Hyperthermia. Preprints2024, 2024011978. https://doi.org/10.20944/preprints202401.1978.v1
Kostyuchenko, E.; Amletova, E. Study of the Influence of Data Volume on the Quality of Regression to Restore the Distribution of Temperatures inside Tissue during Hyperthermia. Preprints 2024, 2024011978. https://doi.org/10.20944/preprints202401.1978.v1
Kostyuchenko, E.; Amletova, E. Study of the Influence of Data Volume on the Quality of Regression to Restore the Distribution of Temperatures inside Tissue during Hyperthermia. Preprints2024, 2024011978. https://doi.org/10.20944/preprints202401.1978.v1
APA Style
Kostyuchenko, E., & Amletova, E. (2024). Study of the Influence of Data Volume on the Quality of Regression to Restore the Distribution of Temperatures inside Tissue during Hyperthermia. Preprints. https://doi.org/10.20944/preprints202401.1978.v1
Chicago/Turabian Style
Kostyuchenko, E. and Elena Amletova. 2024 "Study of the Influence of Data Volume on the Quality of Regression to Restore the Distribution of Temperatures inside Tissue during Hyperthermia" Preprints. https://doi.org/10.20944/preprints202401.1978.v1
Abstract
The use of hyperthermia is one of the effective methods of treating cancer. In this case, the problem arises of constructing and predicting the distribution of the thermal field inside tissues depending on their type. This paper discusses the use of linear and polynomial regression methods, as well as regression algorithms based on decision tree, random forest and K nearest neighbors methods to recover missing temperature values. The influence of the size of the training sample when performing regression on the quality of the reconstructed values is investigated. Experimental recommendations on sample size are given for the selected decision tree and random forest methods. The possibility of a significant (10 times or more) reduction in the initial sample size, without leading to a decrease in the R2 metric by more than 0.05 for various tissues, has been shown.
Keywords
Hyperthermia, Regression, Data reduction, Decision Tree, Random Forest
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
