This article discusses prediction algorithms for the preliminary diagnosis of gastroenterological diseases. Made analysis of several algorithms such as KNN, Random Forest, DNN, ResNet. Currently, artificial intelligence algorithms are widely used in all industries and give their results. For example, it increases the efficiency of the staff and reduces the human factor. With the help of some algorithms, it is possible to solve the problem of diagnosing several diseases. Classification algorithms are used, with the help of classification, the diagnosis system quickly determines the disease and can help the medical staff quickly make a decision and make an accurate diagnosis for a specific disease. The effectiveness of algorithms in the field of medicine of gastroenterological diseases has been tested and the corresponding results have been obtained. A study was conducted on the composition of human saliva, the characteristics and function of saliva. The parameters of human saliva and how it affects human health have been studied. In this case, an analysis of algorithms for the preliminary diagnosis of a disease of the gastrointestinal tract was made. With the help of saliva, you can pre-diagnose diseases of the gastrointestinal tract. For preliminary diagnosis and determination of diseases of the gastrointestinal tract, the classification algorithms KNN, Random Forest, DNN and ResNet are used., {"references":["Балашова, А. Фейки и роботы: какими будут главные технологические тренды 2019.","А. Балашова, А. Посыпкина, Е. Баленко // РБК. – 2018. – 3 дек.","Christopher Bishop, Pattern Recognition and Machine Learning, 2006","Leon Stenneth, Philip S. Yu, Monitoring and mining GPS traces in transit space, SIAM International Conference on Data Mining","Ganesh J., Gupta M., Varma V.Interpretation of Semantic Tweet Representations // arXiv preprint arXiv:1704.00898. — 2017.","Zhang A.,Culbertson B.,Paritosh P.Characterizing Online Discussion Using CoarseDiscourse Sequences // Proceedings of the International AAAI Conference on Web andSocial Media. — 2017","Hastie, T., Tibshirani R., Friedman J. Chapter 15. Random Forests // The Elements of Statistical Learning: Data Mining, Inference, and Prediction. — 2nd ed. — Springer-Verlag, 2009. — 746 p.","Stallkamp J. et al. Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition //Neural networks. - 2012. - Т. 32. - С. 323-332","Masci J. et al. Stacked convolutional auto-encoders for hierarchical feature extraction //Artificial Neural Networks and Machine Learning-ICANN 2011. - Springer Berlin Heidelberg, 2011. - С. 52-59.","Krizhevsky A., Sutskever I., Hinton G. E. Imagenet classification with deep convolutional neural networks //Advances in neural information processing systems. - 2012. - С. 1097-1105","A nanoelectronics-blood-based diagnostic biomarker for myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) https://www.pnas.org/doi/full/10.1073/pnas.1901274116","Микаелян Н.П., Комаров О.С., Давыдов В.В., Мейснер И.С. Биохимия ротовой жидкости в норме и при патологии. Учебно-методическое пособие для самостоятельной работы студентов по специальности «Стоматология». — Москва: ИКАР, 2017. — С. 10–11. — 64 с. — ISBN 978-5-7974-0574-0.","Физиология человека. Учебник. Под ред. В. М. Покровского, Г. Ф. Коротько. — М.: Медицина, 1997 ISBN 5-225-02693-1 т. 2 c. 39","Привес, М.Г. Анатомия человека / М.Г. Привес, Н.К. Лысенков, В.И. Бушкович. – Санкт-Петербург : СПбМАПО, 2011, 2014. – 720 с.","Анатомия человека. В 2 томах. Том 1 [Электронный ресурс]: учебник / Под ред. М.Р. Сапина - М. : ГЭОТАР-Медиа, 2013. - 528 с.","Анатомия человека. В 2 томах. Т. II [Электронный ресурс] : учебник / Под ред. М.Р. Сапина - М. : ГЭОТАР-Медиа, 2013. - 456 с.:","Синельников, Р.Д. Атлас анатомии человека : в 4 т. / Р.Д. Синельников. – Москва : Медицина, 2010.","Островерхов Г.Е. Оперативная хирургия и топографическая анатомия [Текст]: учеб. для мед. вузов / Г.Е. Островерхов, Ю.М. Бомаш, Д.Н. Лубоцкий. – 5-е изд., испр. – М.: МИА, 2013. – 736 с","Топографическая анатомия и оперативная хирургия: учебник / А.В. Николаев. ― 3-е изд., испр. и доп. ― М.: ГЭОТАР-Медиа, 2015. ― 736 с","M. B. Boltaevich, N. R. H. ogli, G. N. S. qizi and M. S. S. ogli, \"Estimation affects of formats and resizing process to the accuracy of convolutional neural network,\" 2019 International Conference on Information Science and Communications Technologies (ICISCT), 2019, pp. 1-5, doi: 10.1109/ICISCT47635.2019.9011858.","Muminov, B., et al. \"Localization and Classification of Myocardial Infarction Based on Artificial Neural Network,(2020) 2020 Information Communication Technologies Conference.\" (2020): 245-249.","Яхшибоева Д.Э., Эрметов Э.Я. Предварительная прогнозирование медицинских заболеваний с помощью нейронных сетей. \"Digitalization is the future of medicine\" (2022). – 317-323 c"]}