Glucose Data Classification for Diabetic Patient Monitoring
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Internet of ThingsBig dataHealthcareMachine learningBlood glucoseDiabetes
Rghioui, A., Lloret, J., Parra, L., Sendra, S., & Oumnad, A. (2019). Glucose Data Classification for Diabetic Patient Monitoring. Applied Sciences, 9(20), 4459.
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-P
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.