Abstract
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 disease 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 results dispersion 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. For a more reliable further diagnosis, machine learning of the biotechnical disease monitoring system was carried out. 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.
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Novikov, V. et al. (2023). Machine Learning of the Biotechnic System for Gastroesophageal Reflux Disease Monitoring. In: Babichev, S., Lytvynenko, V. (eds) Lecture Notes in Data Engineering, Computational Intelligence, and Decision Making. ISDMCI 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 149. Springer, Cham. https://doi.org/10.1007/978-3-031-16203-9_23
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