Analysis and design of Diabetes risk monitoring system based on ensemble learning

At present, the informatization construction in the medical field not only enables medical services to break through the constraints of time and space, but also makes medical services more efficient and scientific. The new generation of information technology has become a revolutionary driving force for traditional medical care to turn to higher-level smart medical care. The number of patients with chronic diseases in China ranks first in the world, and diabetes and related diseases are an important part of it. In order to help residents detect diabetes early, it is necessary to establish a diabetes risk monitoring system to detect high-risk groups. This system can monitor and warn of diabetes, and remind people at risk of diabetes to seek medical treatment as soon as possible. In this paper, by comparing a variety of machine learning algorithms, such as logistic regression, random forest, LightGBM, XGBoost, etc., to find out the algorithm with better performance. Finally, a diabetes risk prediction model was integrated by stacking method, and a diabetes monitoring system based on ensemble learning was designed. The system plays a very good auxiliary role in the treatment of diabetes.