An IoT Based diabetic patient Monitoring System Using Machine Learning and Node MCU

• Published in: Journal of physics. Conference series Vol. 1743; no. 1; pp. 12035 - 12045
• Main Authors: Rghioui, Amine, Naja, Assia, Mauri, Jaime Lloret, Oumnad, Abedlmajid
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.01.2021
• Subjects: Algorithms; Big Data; Body temperature; Diabetes; Internet of Things; Machine learning; Medical research; Monitoring systems; Patients; Physicians; Physics; Telemedicine
• ISSN: 1742-6588; 1742-6596
• DOI: 10.1088/1742-6596/1743/1/012035

Diabetic patient monitoring is a systematic method that provides us with detailed information about the diabetic patient. Diabetic patient monitoring systems play a significant role in monitoring the patient's health, especially with the use of Internet of Things (IoT) devices. Diabetic patient monitoring systems are able basically to monitor diabetic patients and save some data about blood glucose level, body temperature, and location. The role of this system is not limited to patients monitoring, it can also classify data using machine learning techniques. Predictive analytic for diabetic patients is very important due to its ability to help diabetic patients, their families, doctors, and medical researchers to make decisions on diabetic patient treatment based on big data. This paper describes a new system for monitoring diabetic patients and discusses predictive analytics using four different machine-learning algorithms. The performance and accuracy of the applied algorithms are discussed and compared to choose the best one in terms of several parameters.