Сибирский федеральный университет

Тип публикации: доклад, тезисы доклада, статья из сборника материалов конференций

Конференция: International Conference on IT in Business and Industry, ITBI 2021

Год издания: 2021

Идентификатор DOI: 10.1088/1742-6596/2032/1/012027

Аннотация: This article discusses the application of machine learning in the medical field, namely, the classification of acute pancreatitis. This disease was chosen as a priority in the solution for a number of reasons: the presence of subjective indicators on the part of the patient, the requirement for a high speed of provision of the necessary treatment in accordance with the set severity, the lack of the necessary equipment for the correct diagnosis of the severity. Thus, in the course of the study, a classifier was built that recognizes severe and mild severity. With the help of the selection of the model's hyper-parameters, it happened that at each stage of model optimization, the leading kernel was the radial-basis function. Based on the results of the selection of the feature space by the thinning method for each core, sets of features that are different from each other, both in quality and quantity, were identified. Thus, the radial basis function selected for itself the average in quantity and quality between the polynomial kernel and the hyperbolic tangent. At the end of the study, to check the accuracy of the adjusted regressions, a rolling exam was conducted, during which certain indicators were established for the 1vR and 3vR regressions, which led to the conclusion that the first and third class labels are linearly separable, but the second class label cannot be distinguished. Moreover, the results of the two regressions in the aggregate clearly distinguish three clusters. © 2021 Institute of Physics Publishing. All rights reserved.

Журнал: Journal of Physics: Conference Series

Выпуск журнала: Vol. 2032, Is. 1

Номера страниц: 12027

ISSN журнала: 17426588

Издатель: IOP Publishing Ltd

• Mikhalev A.S. (Siberian Federal University, 79, Svobodny pr., Krasnoyarsk, 660041, Russian Federation)
• Kukartsev V.V. (Siberian Federal University, 79, Svobodny pr., Krasnoyarsk, 660041, Russian Federation, Reshetnev Siberian State University of Science and Technology, 31, Krasnoyarsky Rabochy Av., Krasnoyarsk, 660037, Russian Federation)
• Kaizer Yu.F. (Siberian Federal University, 79, Svobodny pr., Krasnoyarsk, 660041, Russian Federation)
• Lysyannikov A.V. (Siberian Federal University, 79, Svobodny pr., Krasnoyarsk, 660041, Russian Federation)
• Stepanova E.V. (Siberian Federal University, 79, Svobodny pr., Krasnoyarsk, 660041, Russian Federation, Krasnoyarsk State Agrarian University, 90, Mira Av., Krasnoyarsk, 660049, Russian Federation)
• Vaitekunaite P.Yu. (Reshetnev Siberian State University of Science and Technology, 31, Krasnoyarsky Rabochy Av., Krasnoyarsk, 660037, Russian Federation)

• Scopus
• Ядро РИНЦ (eLIBRARY.RU)