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Glucose Data Classification for Diabetic Patient Monitoring

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Glucose Data Classification for Diabetic Patient Monitoring

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dc.contributor.author Rghioui, Amine es_ES
dc.contributor.author Lloret, Jaime es_ES
dc.contributor.author Parra-Boronat, Lorena es_ES
dc.contributor.author Sendra, Sandra es_ES
dc.contributor.author Oumnad, Abdelmajid es_ES
dc.date.accessioned 2020-11-03T04:31:04Z
dc.date.available 2020-11-03T04:31:04Z
dc.date.issued 2019-10 es_ES
dc.identifier.uri http://hdl.handle.net/10251/153836
dc.description.abstract [EN] Living longer and healthier is the wish of all patients. Therefore, to design effective solutions for this objective, the concept of Big Data in the health field can be integrated. Our work proposes a patient monitoring system based on Internet of Things (IoT) and a diagnostic prediction tool for diabetic patients. This system provides real-time blood glucose readings and information on blood glucose levels. It monitors blood glucose levels at regular intervals. The proposed system aims to prevent high blood sugar and significant glucose fluctuations. The system provides a precise result. The collected and stored data will be classified by using several classification algorithms to predict glucose levels in diabetic patients. The main advantage of this system is that the blood glucose level is reported instantly; it can be lowered or increased. es_ES
dc.description.sponsorship This work has been partially supported by the "Ministerio de Economia y Competitividad" in the "Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia, Subprograma Estatal de Generacion de Conocimiento" within the project under Grant TIN2017-84802-C2-1-P. es_ES
dc.language Inglés es_ES
dc.publisher MDPI AG es_ES
dc.relation.ispartof Applied Sciences es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Internet of Things es_ES
dc.subject Big Data es_ES
dc.subject Healthcare es_ES
dc.subject Machine learning es_ES
dc.subject Diabetes es_ES
dc.subject Blood glucose es_ES
dc.subject.classification INGENIERIA TELEMATICA es_ES
dc.title Glucose Data Classification for Diabetic Patient Monitoring es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/app9204459 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2017-84802-C2-1-P/ES/RED COGNITIVA DEFINIDA POR SOFTWARE PARA OPTIMIZAR Y SECURIZAR TRAFICO DE INTERNET DE LAS COSAS CON INFORMACION CRITICA/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Comunicaciones - Departament de Comunicacions es_ES
dc.description.bibliographicCitation Rghioui, A.; Lloret, J.; Parra-Boronat, L.; Sendra, S.; Oumnad, A. (2019). Glucose Data Classification for Diabetic Patient Monitoring. Applied Sciences. 9(20):1-15. https://doi.org/10.3390/app9204459 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.3390/app9204459 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 15 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 9 es_ES
dc.description.issue 20 es_ES
dc.identifier.eissn 2076-3417 es_ES
dc.relation.pasarela S\410933 es_ES
dc.contributor.funder Agencia Estatal de Investigación es_ES
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