Copyright: © the author(s), publisher and licensee Technoscience Academy. This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited International Journal of Scientific Research in Computer Science, Engineering and Information Technology ISSN : 2456-3307 (www.ijsrcseit.com) doi : https://doi.org/10.32628/CSEIT217629 146 Integration of IoT and MLA In Prediction of Diabetes : An Overview A. Prathap 1 , Dr. R. Jemima Priyadarsini 2 1 Department of Computer Science, Bishop Heber College (Autonomous), Affiliated to Bharathidasan University, Tiruchirappalli, India 2 Head, Department of Computer Science, Bishop Heber College (Autonomous), Affiliated to Bharathidasan University, Tiruchirappalli, India Article Info Volume 7, Issue 6 Page Number: 146-153 Publication Issue : November-December-2021 Article History Accepted : 20 Nov 2021 Published : 05 Dec 2021 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. Keywords : Diabetes, K-Nearest Neighbor (KNN), Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), Naive Bayes (NB). I. INTRODUCTION Diabetes is now one of the world's most life- threatening diseases. Diabetes is growing more frequently in India, with millions of individuals suffering from the disease. Diabetes mellitus is a serious healthcare problem in India that could reach epidemic levels, and its many complications can cause a slew of problems for patients. The incidence of diabetes has risen dramatically in the last four decades and is anticipated to rise considerably more in the coming decades. The disease currently has no solution,