Glucose Data Classification for Diabetic Patient Monitoring
Metadata
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MDPI
Materia
Internet of Things Big data Healthcare Machine learning Blood glucose Diabetes
Date
2019-10-21Referencia bibliográfica
Rghioui, A., Lloret, J., Parra, L., Sendra, S., & Oumnad, A. (2019). Glucose Data Classification for Diabetic Patient Monitoring. Applied Sciences, 9(20), 4459.
Sponsorship
This 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-PAbstract
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.