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.

该论文提出了一种基于智能健康护理系统的、用于预测 2 型糖尿病的机器学习模型 HealthEdge，并使用真实数据集进行了两种机器学习算法（随机森林和逻辑回归）之间的比较分析，结果表明 RF 预测糖尿病的准确率比 LR 平均高 6％。

基于机器学习的智能医疗框架 HealthEdge: 面向一体化 IoT、Edge 和 Cloud 计算系统中 2 型糖尿病预测的应用

HealthEdge: A Machine Learning-Based Smart Healthcare Framework for Prediction of Type 2 Diabetes in an Integrated IoT, Edge, and Cloud Computing System