Relationship between helicobacter pylori infection and type 2 diabetes using machine learning BPNN mathematical model under community information management

dc.contributor.author Ma, Huan
dc.contributor.author Xiao, Juan
dc.contributor.author Chen, Zhaoxu
dc.contributor.author Tang, Dong
dc.contributor.author Gao, Yuqiang
dc.contributor.author Zhan, Shuhui
dc.contributor.author Ghonaem, Eman
dc.contributor.author Abo Keir, Mohammed Yousuf
dc.date.accessioned 2021-06-17T06:21:33Z
dc.date.available 2021-06-17T06:21:33Z
dc.date.copyright 2021
dc.date.issued 2021-07
dc.description This article is not available at CUD collection. The version of scholarly record of this article is published in Results in Physics (2021), available online at: https://doi.org/10.1016/j.rinp.2021.104363 en_US
dc.description.abstract Objective: This exploration aims to explore the effect of Helicobacter pylori infection on blood glucose mechanism and gastric function in patients with type 2 diabetes under the background of electronic medicine, and the effect of community information management platform on health management efficiency of patients with Helicobacter pylori, so as to lay the foundation for clinical research on Helicobacter pylori participation in the mechanism of type 2 diabetes. Methods: In this paper, 300 patients who were treated in our hospital from June 2016 to June 2019 were selected as the research object. Among them, 228 patients with type 2 diabetes were recorded as the experimental group, and 72 patients with non-type 2 diabetes were recorded as the control group. 13C breath test and serum IgG antibody test were performed on each research object. Patients infected with Helicobacter pylori in the experimental group were divided into groups according to the degree of urine albumin excretion. Urinary albumin excretion rate was <30 mg/24 h as group A, between [30,300]mg/24 h as group B, and greater than 300 mg/24 h as group C. The control group was recorded as group D. Blood biochemical indexes and gastroscopy were detected in the four groups; the blood biochemical indexes of each group were compared and analyzed by statistical software; the artificial intelligence health information platform in community information management was established, and the mathematical prediction model of diabetes was established based on Back Propagation Neural Network (BPNN). Results: The proportion of Helicobacter pylori infection in patients with type 2 diabetes was 60%, and the proportion of Helicobacter pylori infection in the control group was 40%. There was a significant difference in fasting blood glucose indicator and cholesterol indicator between group C and group D, P < 0.05. There was a significant difference in the percentage indicator of glycated protein between group A and group C, P < 0.05. There was a significant difference in normal gastroscopy between group A and group D, P < 0.05. In the process of training, the error of the train set of the mathematical model based on BPNN is gradually reduced, and it has good convergence. When the number of hidden layer units is 3, the AUC (Area Under Curve) of train set is the largest. When the number of hidden layer units is 1, the AUC of the test set is the largest, so the network model with one hidden layer unit is selected. Conclusion: The community use efficiency of each performance of the artificial intelligence health information platform in community information management has been significantly improved compared with that before optimization, which can improve the health management level on the basis of patients' electronic medical information. © 2021 The Author(s) en_US
dc.identifier.citation Ma, H., Xiao, J., Chen, Z., Tang, D., Gao, Y., Zhan, S., Ghonaem, E., & Abo Keir, M. Y. (2021). Relationship between Helicobacter Pylori Infection and Type 2 Diabetes Using Machine Learning BPNN Mathematical Model under Community Information Management. Results in Physics, 26, 104363. https://doi.org/10.1016/j.rinp.2021.104363 en_US
dc.identifier.issn 22113797
dc.identifier.uri https://doi.org/10.1016/j.rinp.2021.104363
dc.identifier.uri http://hdl.handle.net/20.500.12519/392
dc.language.iso en en_US
dc.publisher Elsevier B.V. en_US
dc.relation Authors Affiliations : Ma, H., Department of Gastroenterology, Qingdao Municipal Hospital, Qingdao, Shandong Province 266000, China; Xiao, J., Department of Endocrinology, Qingdao Municipal Hospital, Qingdao, Shandong Province 266000, China; Chen, Z., Respiratory intensive section, Taian City Central Hospital, Taian City, Shandong Province, China; Tang, D., Department of Gastroenterology, Qingdao Municipal Hospital, Qingdao, Shandong Province 266000, China; Gao, Y., Department of Gastroenterology, Qingdao Municipal Hospital, Qingdao, Shandong Province 266000, China; Zhan, S., Department of Gastroenterology, Qingdao Municipal Hospital, Qingdao, Shandong Province 266000, China; Ghonaem, E., Department of Social Sciences/ Clinical Psychology, Faculty of Communication, Arts and Sciences, Canadian University Dubai, Dubai, United Arab Emirates; Abo Keir, M.Y., Applied Science University, Al Eker, Bahrain
dc.relation.ispartofseries Results in Physics;Volume 26
dc.rights Creative Commons CC-BY-NC-ND License
dc.rights.holder Copyright : © 2021 The Author(s)
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Artificial intelligence en_US
dc.subject BPNN en_US
dc.subject Community information management health information platform en_US
dc.subject Helicobacter pylori en_US
dc.subject Mathematical model en_US
dc.subject Type 2 diabetes en_US
dc.title Relationship between helicobacter pylori infection and type 2 diabetes using machine learning BPNN mathematical model under community information management en_US
dc.type Article en_US
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