HealthEdge: A Machine Learning-Based Smart Healthcare Framework for Prediction of Type 2 Diabetes in an Integrated IoT, Edge, and Cloud Computing System
This work addresses diabetes prediction for healthcare applications, but it is incremental as it applies existing machine learning methods to a new integrated system.
The paper tackled predicting type 2 diabetes using a smart healthcare framework called HealthEdge, integrating IoT, edge, and cloud computing, and found that Random Forest achieved 6% higher accuracy on average compared to Logistic Regression in experiments on two real-life datasets.
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.