2021, issue 4, p. 35-42

Received 01.11.2021; Revised 21.11.2021; Accepted 21.12.2021

Published 30.12.2021; First Online 27.01.2022

https://doi.org/10.34229/2707-451X.21.4.4

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MSC 60G05

Analysis of Surface Plasmon Resonance Indicators Using Bayesian Recognition Procedures with Independent Signs in Gliomas, Metastases and Meningiomas

Andrii Tarasov

V.M. Glushkov Institute of Cybernetics of the NAS of Ukraine, Kyiv

Correspondence: This email address is being protected from spambots. You need JavaScript enabled to view it.

 

Introduction. The article discusses the application of Bayesian recognition procedures with independent signs in relation to the data of the plasmon resonance indicators (PRI), which were taken from patients with gliomas, metastases, meningiomas, craniocerebral concussion and from a group of healthy people.

Purpose of the article. Improving the efficiency of recognition of inflammatory processes in gliomas, metastases and meningiomas PRI using optimal recognition procedures with independent signs.

Results. In previous article by the author, an attempt was made to recognize inflammatory processes by the PRI caused by brain cancer using Bayesian recognition procedures based on a single substance. In this work, a new model was built using several independent signs (different substances) at once. The results obtained on the basis of the new model significantly increased their efficiency in relation to the models that were used earlier. Such an increase in all comparisons ranged from 5 to 18%, and up to 88.8%. If earlier it was possible to recognize only combinations of diagnoses in which there were no more than two diagnoses, then in this work it was possible to recognize three diagnoses at once. At the same time, the recognition efficiency became slightly more than 66%. An attempt was also made to recognize more than three diagnoses, but the new model did not give significant results, slightly exceeding 46% when recognizing four diagnoses at once.

Conclusions. Thanks to the use of Bayesian recognition procedures with independent signs, it was possible to significantly increase the recognition of inflammatory processes caused by brain cancer. PRI, which is an auxiliary tool in the diagnosis of gliomas, allows one or another pathology to be determined in the preoperative period, since the pathology is finally determined only when studying a surgically removed tumor. In the postoperative period, such a modification is an indicator of repeated recurrence of gliomas. It was also possible to significantly increase the recognition of inflammatory processes caused by non-oncological disease (traumatic brain injury) in relation to oncological processes in gliomas, metastases and meningiomas.

 

Keywords: Bayesian recognition procedure, independent signs, gliomas, metastases, meningiomas, plasmon resonance indicators, complex parameter.

 

Cite as: Tarasov A. Analysis of Surface Plasmon Resonance Indicators Using Bayesian Recognition Procedures with Independent Signs in Gliomas, Metastases and Meningiomas. Cybernetics and Computer Technologies. 2021. 4. P. 35–42. (in Ukrainian) https://doi.org/10.34229/2707-451X.21.4.4

 

References

           1.     Gridina N.Ya., Gupal A.M., Tarasov A.L., Ushenin Yu.V. Analysis of Neurosurgical Pathologies Using Bayesian Recognition Procedures for Indicators of Surface Plasmon Resonance in the Aggregation of Blood Cells. Cybernetics and System Analysis. 2020. 56. P. 550–558. https://doi.org/10.1007/s10559-020-00271-4

           2.     Tarasov A.L., Gupal A.M., Gridina N.Ya. Modofication of the Use of Bayesian Recognition Procedures for Inflammatory Processes in Gliomas, Metastasis and Meningiomas by Indicators of Erythrocyte Sedimentation Rate. Cybernetics and Computer Technologies. 2021. 2. P. 57–62. https://doi.org/10.34229/2707-451X.21.2.5

           3.     Tarasov A.L. Bayesian Recognition Procedures with Independent Signs of Inflammatory Processes in Gliomas, Metastases and Meningiomas by Indicators of Erythrocyte Sedimentation Rate. Cybernetics and Computer Technologies. 2021. 3. P. 34–42. https://doi.org/10.34229/2707-451X.21.3.3

 

 

ISSN 2707-451X (Online)

ISSN 2707-4501 (Print)

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