<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In the digitalization of big data information, how to use big data and China’s radiation industry combined have become a key topic of social concern. This paper first constructs LightGBM model by gradient boosting tree and deep neural network. Gradient boosting tree optimizes the convergence and stability of LightGBM model, while the deep neural network improves the accuracy and efficiency of the LightGBM model by updating the weights on each edge. Then the radiation safety management method is proposed according to the danger of the radiation industry, the characteristic data of radiation industry scale and efficiency are obtained through research work, the research indexes are determined, and finally, the data analysis of China’s radiation industry scale and efficiency is conducted by using LightGBM model. Take the medical isotope industry as an example: from 2010 to 2020, the number of nuclear medicine industries increased from 857 to 1148, and the deviation rate of prediction was always maintained at 10%. In terms of radiation industry benefits: the annual growth rate of PET diagnostics, SPECT diagnostics and Nuclide Therapy benefit is 56%, 14% and 5.3%, respectively, the radiation industry has great market demand, and the benefit will continue to grow in the future. This study is a comprehensive and accurate analysis of the scale and benefits of China’s radiation industry and has a guiding reference value for the development and research of China’s radiation industry.</p>
</abstract>

In the digitalization of big data information, how to use big data and China’s radiation industry combined have become a key topic of social concern. This paper first constructs LightGBM model by gradient boosting tree and deep neural network. Gradient boosting tree optimizes the convergence and stability of LightGBM model, while the deep neural network improves the accuracy and efficiency of the LightGBM model by updating the weights on each edge. Then the radiation safety management method is proposed according to the danger of the radiation industry, the characteristic data of radiation industry scale and efficiency are obtained through research work, the research indexes are determined, and finally, the data analysis of China’s radiation industry scale and efficiency is conducted by using LightGBM model. Take the medical isotope industry as an example: from 2010 to 2020, the number of nuclear medicine industries increased from 857 to 1148, and the deviation rate of prediction was always maintained at 10%. In terms of radiation industry benefits: the annual growth rate of PET diagnostics, SPECT diagnostics and Nuclide Therapy benefit is 56%, 14% and 5.3%, respectively, the radiation industry has great market demand, and the benefit will continue to grow in the future. This study is a comprehensive and accurate analysis of the scale and benefits of China’s radiation industry and has a guiding reference value for the development and research of China’s radiation industry.

Scale and efficiency of China’s radiation industry based on LightGBM model

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Scale and efficiency of China\u2019s radiation industry based on LightGBM model"}

In the digitalization of big data information, how to use big data and China’s radiation industry combined have become a key topic of social concern. This...

Scale and efficiency of China’s radiation industry based on LightGBM model

Online veröffentlicht: 28. Aug. 2023

Seitenbereich: -

Eingereicht: 17. Okt. 2022

Akzeptiert: 08. März 2022

DOI: https://doi.org/10.2478/amns.2023.2.00257

Schlüsselwörter
LightGBM model, Gradient boosting tree, Deep neural network, Radiation safety, Industrial efficiency, Industrial scale

© 2023 Yu Gong et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Scale and efficiency of China’s radiation industry based on LightGBM model

Online veröffentlicht: 28. Aug. 2023

Seitenbereich: -

Eingereicht: 17. Okt. 2022

Akzeptiert: 08. März 2022

DOI: https://doi.org/10.2478/amns.2023.2.00257

SchlüsselwörterLightGBM model, Gradient boosting tree, Deep neural network, Radiation safety, Industrial efficiency, Industrial scale

© 2023 Yu Gong et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
LightGBM model, Gradient boosting tree, Deep neural network, Radiation safety, Industrial efficiency, Industrial scale