Integration of IoT and MLA In Prediction of Diabetes : An Overview

Abstract

A Healthcare system that employs modern computer techniques is the most investigated area in Research. For many years, researchers in the disciplines of Healthcare have collaborated to improve such systems technologically. A number of Internet-based apps on diabetes management have been proposed as a result of rapid developments in wireless and web technology. According to a recent World Health Organization Survey the number of persons affected with diabetics has increased. Diabetes chronic symptoms are the most common Health Problems. Large volumes of medical data are being created. These patients' health data should be recorded and preserved so that continual monitoring and technology advancements can be used to interpret, learn, and anticipate. Internet of Things (IoT) is used to implement numerous applications. IoT can be used in numerous domains, like the health surveillance system of patients. Various successful machine learning methods can be used to forecast diabetes, allowing people to avoid it and receive treatment as soon as possible. Different machine learning classification algorithms for diabetes are investigated in depth in this work. Machine learning algorithms applied on the diabetes data set include K-Nearest Neighbor (KNN), Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), Naive Bayes (NB), and others.