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dc.contributor.authorRghioui, Amine
dc.contributor.authorLloret, Jaime
dc.contributor.authorParra, Lorena
dc.contributor.authorSendra Compte, Sandra 
dc.contributor.authorOumnad, Abdelmajid
dc.date.accessioned2019-12-05T13:00:54Z
dc.date.available2019-12-05T13:00:54Z
dc.date.issued2019-10-21
dc.identifier.citationRghioui, A., Lloret, J., Parra, L., Sendra, S., & Oumnad, A. (2019). Glucose Data Classification for Diabetic Patient Monitoring. Applied Sciences, 9(20), 4459.es_ES
dc.identifier.urihttp://hdl.handle.net/10481/58223
dc.description.abstractLiving 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.sponsorshipThis work has been partially supported by the “Ministerio de Economía y Competitividad” in the “Programa Estatal de Fomento de la Investigación Científica y Técnica de Excelencia, Subprograma Estatal de Generación de Conocimiento” within the project under Grant TIN2017-84802-C2-1-Pes_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.rightsAtribución 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectInternet of Thingses_ES
dc.subjectBig dataes_ES
dc.subjectHealthcarees_ES
dc.subjectMachine learninges_ES
dc.subjectBlood glucosees_ES
dc.subjectDiabetes es_ES
dc.titleGlucose Data Classification for Diabetic Patient Monitoringes_ES
dc.typejournal articlees_ES
dc.rights.accessRightsopen accesses_ES
dc.identifier.doi10.3390/app9204459


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