Open AccessAn IoT Based diabetic patient Monitoring System Using Machine Learning and Node MCU
Author(s) -
Amine Rghioui,
Assia Naja,
Jaime Lloret Mauri,
Abedlmajid Oumnad
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1743/1/012035
Subject(s) - internet of things , computer science , machine learning , artificial intelligence , node (physics) , remote patient monitoring , patient data , continuous glucose monitoring , analytics , medicine , diabetes mellitus , data mining , engineering , embedded system , database , nursing , structural engineering , glycemic , endocrinology
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.