Research and Implementation of Cloud-Based Intelligent Diagnosis Platform for Chronic Diseases

At present, the problem of chronic diseases in China is becoming more and more prominent, and there is a trend of rejuvenation, among which diabetes, as a common chronic disease, is located in the first place in the world in terms of the number of patients. To alleviate the aforementioned problems, this paper designs a cloud platform for intelligent diagnosis of chronic diseases. The platform is dedicated to helping people effectively prevent chronic diseases and reduce the risk of developing complications. In this study, diabetes prediction models are analyzed from multiple perspectives, firstly, the category imbalance problem is solved by SMOTE-ENN, and then models such as KNN, logistic regression, random forest, SVM, GBDT, and XGBoost are used to classify the samples respectively and the optimization performances of the two hyper-parameter tuning methods, Bayesian and random search, are compared. Experiments show that XGBoost based on Bayesian optimization outperforms other models and can be applied to intelligent diagnosis of diabetes.