  Diabetes Mellitus has no permanent cure to date and is one of the leading
causes of death globally. The alarming increase in diabetes calls for the need
to take precautionary measures to avoid/predict the occurrence of diabetes.
This paper proposes HealthEdge, a machine learning-based smart healthcare
framework for type 2 diabetes prediction in an integrated IoT-edge-cloud
computing system. Numerical experiments and comparative analysis were carried
out between the two most used machine learning algorithms in the literature,
Random Forest (RF) and Logistic Regression (LR), using two real-life diabetes
datasets. The results show that RF predicts diabetes with 6% more accuracy on
average compared to LR.
