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Typ záznamu:
kapitola v odborné knize (C)
Domácí pracoviště:
Ústav pro výzkum a aplikace fuzzy modelování (94410)
Název:
Machine Learning of the Biotechnic System for Gastroesophageal Reflux Disease Monitoring
Citace
Novikov, V., Voronenko, M., Novikova, A., Boskin, O., Tyshchenko, O., Rozov, Y., Bardachov, Y. a Vyshemyrska, S. Machine Learning of the Biotechnic System for Gastroesophageal Reflux Disease Monitoring.
In:
Vsevolod Novikov, Mariia Voronenko, Anastasiia Novikova, Oleg Boskin, Oleksii Tyshchenko, Yuriy Rozov, Yuriy Bardachov, Svitlana Vyshemyrska.
Lecture Notes in Data Engineering, Computational Intelligence, and Decision Making.
1. vyd. Cham, Switzerland: Springer Cham, 2023. s. 387-406. 2022 International Scientific Conference "Intellectual Systems of Decision-Making and Problems of Computational Intelligence?, Proceedings. ISBN 978-3-031-16202-2.
Podnázev
Rok vydání:
2023
Obor:
Obecná matematika
Forma vydání:
Tištená verze
Kód ISBN:
978-3-031-16202-2
Název knihy v originálním jazyce:
Lecture Notes in Data Engineering, Computational Intelligence, and Decision Making
Název edice a číslo svazku:
2022 International Scientific Conference "Intellectual Systems of Decision-Making and Problems of Computational Intelligence?, Proceedings
Místo vydání:
Cham, Switzerland
Název nakladatele:
Springer Cham
Označení vydání (číslo vydání):
1:
Vydáno:
v zahraničí
Autor zdrojového dokumentu:
Vsevolod Novikov, Mariia Voronenko, Anastasiia Novikova, Oleg Boskin, Oleksii Tyshchenko, Yuriy Rozov, Yuriy Bardachov, Svitlana Vyshemyrska
Počet stran:
20
Počet stran knihy:
721
Strana od:
387
Strana do:
406
Počet výtisků knihy:
500
EID:
2-s2.0-85138672242
Klíčová slova anglicky:
Monitoring, Gastroesophageal reflux disease, Heartrate, Machine learning
Popis v původním jazyce:
The article is devoted to the study of gastroesophageal reflux disease development. The main research contribution is that the study implements prognostic, morpho-functional models to automate the differential diagnostics process. Also, the research developed a special methodology for automating the differential diagnostics process using artificial neural networks based on predictive morpho-functional models. The system analysis method was applied. This method allows you to study analyzed problems and diseases at various systems organization levels, including macro and micro levels to highlight the characteristics, symptoms, syndromes, and signs necessary for private diagnosis, and in the study, the use of algorithms for evaluating the dispersion of the results was further developed, which made it possible to assess the informativeness of signs about the corresponding nosological disease form. The methods and techniques for treating the disease were analyzed. A faster and more reliable method was proposed for monitoring the food effect on the gastroesophageal reflux disease reaction. Statistical processing of the research results is carried out. The reliability of the data is shown. Machine learning of the biotechnical disease monitoring system was carried out for a more reliable further diagnosis. The machine is properly trained and classifies the image. Regression analysis showed the model reliability built using machine learning. After conducting experiments and subsequent analysis of the results, we obtained an accuracy of 99%. The system has correctly learned to classify data. Regression analysis showed an almost linear regression.
Popis v anglickém jazyce:
Seznam ohlasů
Ohlas
R01:
RIV/61988987:17610/23:A2402GZX
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