A Machine Learning Approach For Glucose Level Monitoring Using Classification Algorithms

Abstract

Patients everywhere hope to live a long, healthy life. As a result, the idea of Big Data can be incorporated into the field of medicine in order to build effective strategies for the purpose of achieving this goal. In this article, we present a system for the monitoring and
tracking of diabetes patients that is based on the Internet of Things (IoT), combined with a diagnostic prediction model. The system displays current blood sugar levels and other relevant data in real time. It performs periodic checks on the user's glucose levels. The
suggested system is meant to forestall both hyperglycaemia and drastic swings in blood sugar levels. A precise answer can be obtained from the system. In order to forecast diabetics' blood sugar levels, we will classify the collected and stored data using a variety of classification algorithms. The greatest benefit of this system is the speed with which blood glucose levels are reported and adjusted.