Determination of predisposition to diabetes mellitus based on Fuzzy neural network.
This article solves the problem of determining the occurrence of diabetes mellitus in pregnant women. The aim of the study is to find an effective method based on the machine learning method, which will simplify the procedure and increase the speed of determining this disease with a high degree of a...
| Publicado en: | Revista Latinoamericana de Hipertensión Vol. 17; no. 5; pp. 371 - 376 |
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| Autores principales: | , , , |
| Formato: | Artículo |
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Revista Latinoamericana de Hipertension
2022
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=lth&AN=160573671&site=ehost-live header: @attributes: shortDbName: lth uiTerm: 160573671 longDbName: MedicLatina uiTag: AN controlInfo: bkinfo: jinfo: jid: 18564550 FICN jtl: Revista Latinoamericana de Hipertensión issn: 18564550 maglogo: N pubinfo: dt: 2022 vid: 17 iid: 5 pid: 81901 pub: Revista Latinoamericana de Hipertension artinfo: ui: 160573671 10.5281/zenodo.7372918 ppf: 371 ppct: 5 formats: fmt: @attributes: type: P size: 279KB tig: atl: Determination of predisposition to diabetes mellitus based on Fuzzy neural network. aug: au: Ismagilov, Ilyas Idrisovich Kataseva, Dina Vladimirovna Katasev, Alexey Sergeevich Barinova, Anastasia Olegovna affil: Doctor of Technical Sciences, Professor of Economic Theory and Econometrics Department of the Institute of Management, Economics and Finance of Kazan (Volga Region) Federal University Graduate Student and Senior Lecturer of Information Security Systems Department of the Institute of Computer Technologies and Information Security of Kazan National Research Technical University named after A. N. Tupolev-KAI Doctor of Technical Sciences, Professor of Information Security Systems Department of the Institute of Computer Technologies and Information Security of Kazan National Research Technical University named after A. N. Tupolev-KAI Master's Student of Information Security Systems Department of the Institute of Computer Technologies and Information Security of Kazan National Research Technical University named after A. N. Tupolev-KAI su: Diabetes Fuzzy neural networks Pregnant women Machine learning sug: subj: Diabetes Fuzzy neural networks Pregnant women Machine learning keyword: aprendizaje automático base de conocimientos clasificación classification diabetes mellitus diagnóstico diagnostics fuzzy neural network knowledge base machine learning modelado modeling red neuronal difusa aprendizaje automático base de conocimientos clasificación diagnóstico modelado red neuronal difusa ab: This article solves the problem of determining the occurrence of diabetes mellitus in pregnant women. The aim of the study is to find an effective method based on the machine learning method, which will simplify the procedure and increase the speed of determining this disease with a high degree of accuracy. The currently existing methods for determining diabetes mellitus cannot allow constantly monitoring the presence of diabetes mellitus in pregnant women due to the complexity of the procedure, as well as the long time it takes to process the analysis results. Therefore, the use of modern technologies, in particular machine learning methods, will get rid of these disadvantages and allow to continuously monitor the possible occurrence of the disease. In this article, the subject area is analyzed and the relevance of the research topic is considered, the initial data preparation and collection for a neuro-fuzzy model constructing are carried out. In addition, intelligent models based on various machine learning methods have been constructed. The best method based on a fuzzy neural network has been chosen, which allows to classify the available data with a high degree of accuracy. A use-case diagram for solving practical problems of determining the presence of diabetes mellitus has been developed. Este artículo resuelve el problema de determinar la aparición de diabetes mellitus en mujeres embarazadas. El objetivo del estudio es encontrar un método eficaz basado en el método de aprendizaje automático, que simplificará el procedimiento y aumentará la velocidad de determinación de esta enfermedad con un alto grado de precisión. Los métodos existentes actualmente para la determinación de diabetes mellitus no pueden permitir monitorear constantemente la presencia de diabetes mellitus en mujeres embarazadas debido a la complejidad del procedimiento, así como al largo tiempo que lleva procesar los resultados de los análisis. Por lo tanto, el uso de tecnologías modernas, en particular métodos de aprendizaje automático, eliminará estas desventajas y permitirá monitorear continuamente la posible aparición de la enfermedad. En este artículo se analiza el área temática y se considera la relevancia del tema de investigación, se realiza la preparación y recolección de datos iniciales para la construcción de un modelo neuro-borroso. Además, se han construido modelos inteligentes basados en varios métodos de aprendizaje automático. Se ha elegido el mejor método basado en una red neuronal difusa, que permite clasificar los datos disponibles con un alto grado de precisión. Se ha desarrollado un diagrama de casos de uso para resolver problemas prácticos de determinación de la presencia de diabetes mellitus. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Copyright of Revista Latinoamericana de Hipertensión is the property of Revista Latinoamericana de Hipertension and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. item: Revista Latinoamericana de Hipertensión holder: Revista Latinoamericana de Hipertension dt: @attributes: year: 2022 holdings: @attributes: islocal: N |
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